TY - JOUR A1 - Reidelbach, Marco A1 - Weber, Marcus T1 - MaRDMO: FAIR Documentation of In Silico Research JF - Proceedings in Applied Mathematics and Mechanics N2 - MaRDMO is a plugin for the Research Data Management Organiser (RDMO) that enables the structured, FAIR-compliant documentation, and discovery of mathematical research data. By embedding mathematics-specific questionnaires into a widely used data management plan (DMP) tool, MaRDMO lowers the technical barrier to contributing to and querying the MaRDI Knowledge Graph for researchers across all disciplines, removing the need for knowledge of Wikibase, SPARQL, the underlying ontologies, or other knowledge graph infrastructure. This paper presents the current state of MaRDMO, including its questionnaire-driven documentation process for mathematical models, algorithms, and interdisciplinary workflows, illustrated through a concrete example based on a solver comparison study for the Stokes?Darcy system. We further describe the dedicated Mathematical Research Data Initiative (MaRDI) RDMO instance as a ready-to-use entry point for the community, and discuss recent developments including a simplified basic model catalog, which lowers the content barrier for first-time model documentation, and an improved class-filtered search. The paper concludes with an outlook on LLM-assisted documentation features currently under development, aimed at further addressing the content barrier when documenting complex mathematical research data in full detail. Y1 - 2026 U6 - https://doi.org/10.1002/pamm.70187 VL - 26 IS - 4 SP - e70187 ER - TY - CHAP A1 - Buchholz, Annika A1 - Zittel, Janina A1 - Koch, Thorsten T1 - Robust Unit Commitment in District Heating Networks: Chance-Constrained and CVaR Optimization Under Demand Uncertainty T2 - International Conference on Operations Research 2026 N2 - While district heating networks are a key component of the energy transition, their operational planning is challenging due to substantial uncertainty in heat demand. We address this volatility without compromising system reliability. By adapting chance-constrained programming (CC) and conditional value-at-risk (CVaR) optimization to the mixed-integer unit-commitment problem for district-heating networks, we can compute optimal unit-commitment schedules under demand uncertainty. We generate heat demand time series using a Bayesian model, which explicitly quantifies forecasting uncertainty and yields predictive distributions as input to the network optimization. To handle the inherent uncertainty in heat demand, we apply both robust optimization approaches: chance-constrained programming limits the probability of unmet demand, while CVaR optimization penalizes severe shortfalls in the tail of the distribution. We evaluate the approaches both on real-world data from the Berlin district heating network and on a benchmark set of synthetic, realistically parameterized instances of varying sizes. The results compare the two uncertainty-handling methods with respect to solution quality, risk exposure, and computational effort. Y1 - 2026 ER - TY - JOUR A1 - Vanaret, Charlie A1 - Montoison, Alexis T1 - Uno: a composable framework for nonlinearly constrained optimization JF - The Journal of Open Source Software N2 - Uno is a composable software framework for nonlinearly constrained optimization written in modern C++. It unifies the workflows of Lagrange-Newton methods, i.e., gradient-based algorithms that iteratively solve the KKT optimality conditions using Newton’s method. The central idea is to decompose these methods into reusable, interchangeable components (constraint reformulation, step computation, globalization techniques, and acceptance criteria) so that classical and hybrid algorithms can be assembled, compared, and tested within a single framework rather than reimplemented as separate solvers. As of July 2026, Uno supports sequential (convex and nonconvex) quadratic programming, interior-point (barrier) methods, sequential linear programming, and unconstrained optimization. For full mathematical details of the algorithms implemented in Uno, see Vanaret & Leyffer (2026). Uno has interfaces to Julia, Python, C, Fortran, and AMPL, enabling interoperability across scientific computing environments. Precompiled artifacts are available on GitHub, and the solver can be accessed directly via UnoSolver.jl in Julia or unopy in Python. Y1 - 2026 U6 - https://doi.org/https://doi.org/10.21105/joss.10229 VL - 11 IS - 123 SP - 10229 ER - TY - JOUR A1 - Klenert, Nicolas A1 - Loba-Gómez, Patricia A1 - Abdal Al, Leiss A1 - Gallego Nicolás, Juan Diego A1 - Phelippeau, Harold A1 - Seghiri, Rachida A1 - Martinez-Sanchez, Antonio A1 - Baum, Daniel T1 - Accurate triangle meshes of biological membranes through ridge surface reconstruction JF - 16th Eurographics Workshop on Visual Computing for Biology and Medicine N2 - The geometric reconstruction of biological membranes from electron microscopy data, such as three-dimensional images acquired by cryo-electron tomography, is of great importance for the analysis of cellular environments. Recent advances in deep learning-based approaches enable the segmentation of biological membranes in those data with unprecedented quality and completeness, resulting in highly complex morphological structures. However, in order to fully understand the geometric properties of membranes and how they relate to other cellular structures, including membrane proteins, an explicit geometric representation in the form of triangle meshes is necessary. Here, we present a ridge surface-based approach that is able to transform a wide variety of membrane morphologies, given as segmented voxel representations of membranes, into high-quality triangle meshes. We compare our approach with MidSurfer, a recently published method, which not only fails for those complex morphologies but is also at least one order of magnitude slower than the presented approach. In addition, we test a crease surface extraction algorithm and a medial surface skeleton extraction method on the data presented, and discuss the pros and cons of each approach. Y1 - 2026 U6 - https://doi.org/10.1016/j.cag.2026.104709 ER - TY - JOUR A1 - Farhadifar, Reza A1 - Fabig, Gunar A1 - Wu, Hai-Yin A1 - Young, Yuan-Nan A1 - Vogel, Justus A1 - Needleman, Daniel J. A1 - Müller-Reichert, Thomas A1 - Shelley, Michael J. T1 - Geometric optics organizes organelle interactions and positioning JF - Nature Cell Biology N2 - Abstract Microtubule asters are central to positioning organelles and organizing intracellular architectures. We show that the multi-stage choreography of microtubule asters which positions pronuclei during the first cell division in Caenorhabditis elegans , is governed by a cellular analog of geometric optics. Large-scale electron tomography reveals that astral microtubules reach cortical and pronuclear surfaces largely along line-of-sight trajectories, leaving complementary regions shadowed and inaccessible. Laser ablation identifies surface-anchored motors pulling on microtubules as the dominant drivers of motion. We develop a biophysical model incorporating dynamic terminator curves that separate microtubule-accessible and -inaccessible surfaces, and quantitatively recapitulate aster separation, pronuclear migration, centering, and rotation in control and genetically perturbed embryos. More broadly, robust positioning emerges from a feedback loop wherein intracellular geometry gates microtubule access, thus sculpting the distribution and magnitude of pulling forces, which in turn reshape that geometry. Y1 - 2026 U6 - https://doi.org/https://doi.org/10.64898/2026.04.28.721423 PB - openRxiv ER - TY - CHAP A1 - Brence, Blaž A1 - Ahmed, Maria A1 - Khawari, Sajjad A1 - Baum, Daniel ED - Krueger, Robert ED - Mörth, Eric ED - Furmanová, Katarína T1 - An end-to-end framework for quantifying the exploratory behavior of multicolumnar neurons in Drosophila T2 - Eurographics Workshop on Visual Computing for Biology and Medicine N2 - The analysis of complex imaging data often represents a significant barrier to progress in neurobiology. This is particularly true for the analysis of cells with a complex morphology, and when the imaged data is multi-dimensional. Here, we present a holistic workflow for 4D quantitative analysis of the morphogenesis of a columnar neuron in the developing Drosophila melanogaster brain. The example data shows a neuron that simultaneously contacts tens of columns in the visual neuropil, posing a significant challenge of resolving spatially heterogeneous contact dynamics across individual columns. For successful quantification, the 4D multi-channel imaging data undergoes several processing steps, including data standardization, alignment, discretization, and imaging angle correction. From the processed data, contact dynamics at the level of individual columns are extracted. To facilitate the analysis of results, we developed the Column Explorer tool, which enables the user to visualize and explore the results of the workflow. In addition, analysis of the time requirements for the execution of the workflow is provided for three datasets. The quantitative framework presented here is adaptable to other neural systems with columnar organization, thus providing a generalizable tool for studying contact dynamics during neural circuit development. Y1 - 2026 ER - TY - CHAP A1 - Zommere, Margarita A. A1 - Colautti, Maja A1 - Fanella, Elena A1 - Duquennoy, Rocco A1 - Burger, Sven A1 - Betz, Fridtjof A1 - Rosadoni, Elisabetta A1 - Ottomaniello, Andrea A1 - Mattoli, Virgilio A1 - Toninelli, Costanza T1 - Cavity-enhanced organic single-photon emitters T2 - Proc. SPIE Y1 - 2026 U6 - https://doi.org/10.1117/12.3099825 VL - PC14076 SP - PC140760U ER - TY - JOUR A1 - Hebenstreit, Tim A1 - Gimeno Balaguer, Jaime A1 - Betz, Fridtjof A1 - Binkowski, Felix A1 - Colautti, Maja A1 - Renger, Jan A1 - Toninelli, Costanza A1 - Burger, Sven A1 - Götzinger, Stephan T1 - Design of monolithic microcavities for enhancing organic quantum emitters JF - ArXiV Y1 - 2026 SP - arXiv:2608.12081 ER - TY - JOUR A1 - Konstantin, Mirko A1 - Zachow, Stefan A1 - Mukhopadhyay, Anirban T1 - Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learning JF - Proc. of the 37th British Machine Vision Conference (BMVC) N2 - Federated learning (FL) enables collaborative model training across distributed clients while preserving data privacy by keeping data local. A central challenge in FL is determining which client updates are beneficial for aggregation with respect to each client’s target domain. Existing methods typically address this problem in parameter space by comparing model parameters or gradients. However, parameter-space similarity is often a poor proxy for predictive behavior, particularly in heterogeneous settings where client data is not independent and identically distributed. As a result, updates that are misaligned with a client’s target domain, including those arising from heterogeneous data distributions or malfunctioning clients, may be incorporated into aggregation and degrade local model performance. In this work, we propose Local Inference Guided Aggregation for Heterogeneous Training Environments to Yield Enhancement Through Agreement and Regularization (LIGHTYEAR), a federated learning framework that performs update selection in the function space. Central to our method is an NTK-based agreement score that characterizes predictive behavior and is used to determine the optimal aggregation set for each client. By relating model parameters to local predictive responses, the Neural Tangent Kernel (NTK) enables a function-space characterization of model updates and provides a more expressive criterion for update selection than parameter-space similarity alone. Since access to function-space information before aggregation is not available in conventional centralized FL, LIGHTYEAR leverages a peer-to-peer (P2P) topology, where clients exchange updates directly and can locally evaluate incoming models on private validation data. This enables each client to construct a personalized aggregation set consisting only of updates that are beneficial with respect to its own target domain. The selected updates are then aggregated using a regularized aggregation rule that further stabilizes training under heterogeneity. Our empirical evaluation across five datasets and nine baseline methods demonstrates that LIGHTYEAR consistently outperforms both centralized FL baselines and existing P2P approaches. Y1 - 2026 ER - TY - JOUR A1 - Donati, Luca A1 - Safarov, Fazil A1 - Chewle, Surahit A1 - Weber, Marcus T1 - Variational estimation of generator invariant subspaces JF - The Journal of Chemical Physics N2 - We present VEGIS (Variational Estimation of Generator Invariant Subspaces), a variational method to approximate invariant subspaces of the infinitesimal generator of reversible diffusion processes. The method represents a trial subspace by neural networks and optimizes a Dirichlet-form trace objective that can be evaluated using only equilibrium samples and gradients of the network outputs. After training, the learned trial space can be diagonalized to recover generator eigenfunctions and eigenvalues or transformed by PCCA+ to obtain membership functions associated with metastable sets. In addition, we introduce a VEGIS-driven sampling strategy in which a rough approximation of the dominant slow mode is used to modify the effective diffusivity while preserving the invariant density. Numerical results on low-dimensional and molecular systems demonstrate the accuracy and flexibility of VEGIS. KW - Invariant subspaces KW - variational principle KW - markov process KW - molecular dynamics Y1 - 2026 U6 - https://doi.org/10.1063/5.0341109 SN - 0021-9606 VL - 165 IS - 4 PB - AIP Publishing ER - TY - JOUR A1 - Fackeldey, Konstantin A1 - Schütte, Christof T1 - Closed-Loop Generative Selection: Convergence, Memory, and Noisy Oracles JF - arXiv N2 - Closed-loop generative selection has become a workhorse of computational drug discovery: a learned generative model proposes candidate molecules, a fitness oracle scores them, the best are kept, and the model is retrained on this elite set before the next round. Despite its wide use, the method has lacked a rigorous convergence theory, largely because retraining the model each round breaks the Markov property on which classical evolutionary-algorithm analysis relies. We develop a self-contained theory of convergence and expected running time for this class of algorithms. By recovering a Markov structure on an enlarged state space, we show that elitism makes the search absorbing, and we prove almost-sure convergence together with a runtime bound that decomposes the search into the time spent escaping each fitness level. We then analyse the role of the model's memory---how much of the past it is trained on. When learning improves steadily with more data, deeper memory never hurts; when it does not, an exit-time analysis pinpoints the optimal memory depth and shows that excess memory can actually slow convergence. The theory extends to multi-objective search and to noisy oracles: we quantify how many repeated evaluations certify progress under light-tailed noise, and how robust estimators restore guarantees under heavy tails. Recast in terms of oracle evaluations - the true bottleneck in drug design - the analysis yields a concrete, evaluation-minimal strategy. Areproducible study confirms the predictions, including the surprising cost of excess memory. We close with three open problems. Y1 - 2026 U6 - https://doi.org/https://doi.org/10.48550/arXiv.2607.22211 ER - TY - CHAP A1 - Witzke, Joel A1 - Lößer, Ansgar A1 - Bountris, Vasilis A1 - Wies, Tobias A1 - Schintke, Florian A1 - Scheuermann, Björn T1 - Low-Level I/O Monitoring for Scientific Workflows T2 - 2026 25th International Symposium on Parallel and Distributed Computing (ISPDC) Y1 - 2026 U6 - https://doi.org/10.1109/ISPDC69862.2026.00019 SP - 84 EP - 92 PB - IEEE ER - TY - CHAP A1 - Wang, Cheng A1 - Soto, Carlos J T1 - Recursive Fréchet Mean Estimation T2 - 42nd Conference on Uncertainty in Artificial Intelligence N2 - Estimating the mean of manifold-valued data is a central problem in modern statistics, yet it remains challenging due to the lack of a closed-form expression for the Fréchet mean. The gradient descent algorithm is widely used to approximate this quantity across various applications. Although generally effective, it can be computationally intensive for large datasets, as each iteration requires evaluating gradients with respect to the entire dataset. To address these limitations, we propose a tree-based, Recursive Fréchet Mean Estimator (RFME), tailored to data on manifolds. The proposed method leverages a hierarchical aggregation strategy to reduce computational complexity while preserving statistical accuracy. We establish the weak consistency of RFME with respect to the population Fréchet mean and discuss its computational properties. Through simulation studies and real-world applications, we demonstrate that RFME achieves competitive estimation accuracy with substantially improved efficiency. Moreover, as a generalization of the incremental Fréchet mean estimator, RFME also offers enhanced flexibility while maintaining practical advantages. Y1 - 2026 ER - TY - CHAP A1 - Kulkarni, Aditya A1 - Soto, Carlos T1 - Differentially Private Geodesic Regression T2 - Proceedings of the 43rd International Conference on Machine Learning N2 - In statistical applications it has become increasingly common to encounter data structures that live on non-linear spaces such as manifolds. For data living on such non-linear spaces geodesic regression emerged as a natural extension of linear regression where the response variable lives on a Riemannian manifold. The parameters of geodesic regression capture the relationship of sensitive data, and hence, one should consider the privacy protection practices of said parameters. We consider releasing Differentially Private (DP) parameters of geodesic regression via the K-Norm Gradient (KNG) mechanism for Riemannian manifolds. We derive theoretical bounds for the sensitivity of the parameters showing they are tied to their respective Jacobi fields and hence the curvature of the space. We demonstrate the efficacy of our methodology on the sphere, $S^2 \subset \mathbb{R}^3$, the space of symmetric positive definite matrices, and Kendall's planar shape space. Our methodology is general to any Riemannian manifold, and thus it is suitable for data in domains such as medical imaging and computer vision. Y1 - 2026 ER - TY - CHAP A1 - Mayer, Julius A1 - Dietrich, Laura A1 - von Tycowicz, Christoph T1 - Modeling Trajectories in Neolithic Grinding Stones: Experimental and Computational Shape Analysis at Göbekli Tepe T2 - Eurographics Workshop on Graphics and Cultural Heritage N2 - Grinding stones are widely distributed across prehistoric contexts, yet linking their use to specific processing strategies and food products remains challenging. This study introduces a methodological approach that combines extended experimental replication with computational shape analysis to investigate the long-term morphological change in grinding tools. Focusing on the Early Neolithic site of Göbekli Tepe (9600-8000 BC), we analyze one of the earliest large-scale assemblages of grinding stones associated with cereal processing. Experimental produced replicas of original handstones made of basaltic rocks were used to process einkorn under controlled grinding motions, generating coarse and fine flour through contrasting movement and pressure regimes. Successive wear states were captured using high-resolution photogrammetry and reconstructed as three-dimensional models, allowing cumulative deformation trajectories to be quantified and compared. The results demonstrate that long-term shape alteration reflects consistent patterns linked to grinding behavior and product characteristics. The proposed framework extends functional interpretation beyond surface wear alone and offers a transferable analytical model applicable to other tool classes and archaeological contexts, supporting comparative and computational studies of food-processing technologies in cultural heritage research. Y1 - 2026 ER - TY - CHAP A1 - Klenert, Nicolas A1 - Baum, Daniel T1 - Digital Unfolding of Writing Materials T2 - The Oxford Handbook of Digital Classical Studies N2 - This chapter describes the process of digitally unfolding rolled and folded written documents of different materials such as parchment, papyrus, paper, silver, or lead. The folded state of the documents often conceals the information contained in them from the observer, making it difficult to access. Digital unfolding is necessary to prevent damage to the precious historical artifacts, which might occur if one tried to unfold the documents physically to access hidden information. The first step in this digital process is the acquisition of three-dimensional (3D) image data from the document itself, followed by the segmentation of the writing material in the 3D image. Afterward, the segmented material is digitally flattened to obtain an unobscured view of the script. Finally, visualization of the flattened document allows one to make the writing visible. Each of these steps presents distinct difficulties. This chapter outlines the general workflow, together with some key strategies available to overcome these obstacles. Finally, it discusses some remaining core issues and challenges. Y1 - 2026 U6 - https://doi.org/10.1093/9780197835210.003.0013 SP - 391 EP - 402 PB - Oxford University Press ER - TY - JOUR A1 - Okafornta, Chukwuebuka William A1 - Farhadifar, Reza A1 - Fabig, Gunar A1 - Wu, Hai-Yin A1 - Köckert, Maria A1 - Vogel, Martin A1 - Baum, Daniel A1 - Haase, Robert A1 - Shelley, Michael J. A1 - Needleman, Daniel J. A1 - Müller-Reichert, Thomas T1 - Cell size reduction drives spindle scaling but not chromosome segregation in C. elegans JF - Nature Communications Y1 - 2026 U6 - https://doi.org/10.1038/s41467-026-76360-3 VL - 17 ER - TY - JOUR A1 - Moewis, Philippe A1 - Ehrig, Rainald A1 - Krahl, Leonie A1 - Trepczynski, Adam A1 - Tykfer, Jonas A1 - Hommel, Hagen A1 - Duda, Georg N. T1 - Lateral-stabilized total knee arthroplasty enables design-specific kinematic behavior consistent with key physiological characteristics during flexion JF - Front. Bioeng. Biotechnol. N2 - Anterior knee pain (AKP) remains as one of the most cited causes of patient’s dissatisfaction after total knee arthroplasty (TKA) with non-physiological knee joint kinematics patterns during flexion and patellar maltracking being frequently associated with it. Incorporating in vivo knee joint kinematics into the design of new TKA systems could significantly reduce these negative outcomes. This study aims to show the outcome of a new TKA system developed considering previously assessed in vivo knee joint kinematics. Retrospective study design. Eleven patients treated with the 5C® LATic TKA system were included. In-vivo fluoroscopic measurements in loaded lunge to collect the 3D TKA motion from extension to maximal flexion was conducted at 12 months after index surgery. All patients answered the KSS, FJS and HFKS questionnaires. The kinematics of the 5C® LATic system were characterized by a lateral stability as well as reduced anterior movement in the medial compartment, resulting in an external rotation of the femoral component of the tibia plateau. Moreover, the frontal points reveal a consistent posterior movement of the frontal aspect until maximal achieved flexion. All patients showed improvements in all administered questionnaires compared to their preoperative state, with high overall satisfaction reported for the prosthesis. The results show an expected design-based kinematics. Although the posterior translation of the frontal aspect of the femoral component can only be interpreted as an indirect measure of patellar movement, the increased motion observed, together with the external rotation on the tibia plateau, may contribute to improvement in patellar tracking, which is recognized as a direct contributor to AKP Y1 - 2026 U6 - https://doi.org/https://doi.org/10.3389/fbioe.2026.1804325 VL - 14 ER - TY - JOUR A1 - Zierke, Julian N. A1 - Moewis, Philippe A1 - Ehrig, Rainald A1 - Taylor, William R. A1 - Trepczynski, Adam A1 - Duda, Georg N. A1 - Damm, Philipp T1 - In vivo Knee Joint Friction after Total Knee Arthroplasty is Highly Dynamic and Phase-Dependent JF - Front. Bioeng. Biotechnol. Y1 - 2026 U6 - https://doi.org/10.3389/fbioe.2026.1885711 VL - 14 ER - TY - THES A1 - Ebert, Patricia T1 - Stochastic Optimization and Dispatching Rules for Railway Delay Management N2 - In railway traffic, delays occur every day, causing deviations from the planned timetable and potentially leading to significant delays for passengers, crew members, and trains. In such cases, the original timetable may become infeasible. Therefore, the delay management problem focuses on the construction of a disposition timetable that minimizes the inconvenience for passengers. In this context, two dispatching decisions must be made: wait–depart decisions, which determine whether trains should wait for delayed feeder trains in order to maintain passenger transfers, and precedence decisions, which define the order of trains while respecting the limited capacity of the railway network. For the extensively researched Offline Delay Management Problem (ODM), it is assumed that source delays are fully known and stable over an extended planning period. In this thesis, we relax this assumption and propose the Stochastic Delay Management Problem (DM), which handles source delays as constant within an initial control horizon and as random variables following a delay distribution thereafter. We introduce a two-stage stochastic mixed-integer linear programming formulation for the problem and prove NP-hardness for a special case. Moreover, we analyze different fixations of the wait-depart and precedence decisions. For those, we derive bounds for optimal solutions as well as for the optimal objective values, including tightness results. In practice, numerous simple dispatching rules are applied to guide decisions. To achieve comparability with DM, we integrate seven different rules for wait-depart and precedence decisions into our optimization framework. We then study the price of the rules and show that each examined rule can lead to arbitrarily poor disposition timetables compared to DM. In particular, we present an algorithm corresponding to the case where no dispatching is performed and study the price of non-dispatching. We compare the performance of DM to the performance of the Offline Delay Management Problem and the seven dispatching rules in a computational experiment for the Berlin S-Bahn with different delay scenarios. It becomes evident that taking stochastic delays into account yields on average 1.7% better disposition timetables, but at the cost of higher runtime. Additionally, the average price of non-dispatching of 2.7% indicates that dispatching is desirable, but not as much as one might expect. Three dispatching rules achieve similarly satisfactory results compared to DM. In particular, the rule that neglects all wait-depart decisions finds similarly good or slightly better disposition timetables than DM within a marginally shorter average runtime. KW - delay management KW - timetabling KW - stochastic optimization KW - mixed-integer linear programming Y1 - 2026 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-104000 ER - TY - JOUR A1 - Choi, Gary P. T. A1 - Dao Duc, Khanh A1 - Faigenbaum-Golovin, Shira A1 - Habermann, Karen A1 - Hartman, Emmanuel A1 - von Tycowicz, Christoph A1 - Zhang, Chi A1 - Zhao, Wenjun A1 - Zhou, Felix T1 - Learning the Geometry of Data: A Mathematical Review of Shape Space Analysis N2 - A central objective of machine learning is to identify structure and patterns in data. Advances in data acquisition have increasingly produced datasets whose observations possess rich geometric form, giving rise to shape spaces that encode variability in object geometry. Such datasets arise across a wide range of disciplines, including biology, medicine, anthropology, and computer vision, where subtle geometric differences often carry important scientific information. Traditional machine learning methods, however, are frequently ill-equipped to account for the nonlinear geometric structure underlying these data. This survey synthesizes a rapidly growing body of work on shape space analysis, which provides a mathematical and computational framework for the study of geometric data. Drawing on ideas from differential geometry, statistics, and machine learning, we organize the literature around a common analytical pipeline: shape representation and parameterization, the rigorous construction of robust geodesic metrics, statistical analysis on shape spaces, and geometry-aware learning methods. We discuss how these tools enable the characterization of shape variability, the comparison of geometric objects, and the analysis of structural trajectories across populations and time. To illustrate the breadth of the field, we highlight applications spanning multiple scales of biological organization, including studies of subcellular morphology and primate tooth evolution. Across these and many other domains, researchers face common challenges arising from complex, nonlinear, and often unaligned geometric variation. The review concludes by identifying key theoretical and computational challenges, as well as emerging opportunities driven by increasingly large and diverse geometric datasets. Y1 - 2026 ER - TY - JOUR A1 - Clausecker, Robert A1 - Schintke, Florian T1 - Parallel O(√n) Overhead LSD Radix Sort JF - CoRR Y1 - 2026 U6 - https://doi.org/10.48550/arXiv.2607.05302 VL - abs/2607.05302 SP - 1 EP - 16 ER - TY - JOUR A1 - Clausecker, Robert A1 - Lemire, Daniel T1 - Fixing ill-formed UTF-16 strings with SIMD instructions JF - CoRR Y1 - 2026 U6 - https://doi.org/10.48550/arXiv.2601.06349 VL - abs/2601.06349 SP - 1 EP - 18 ER - TY - JOUR A1 - Clausecker, Robert A1 - Kurpicz, Florian A1 - Palanga, Etienne T1 - Practical Parallel Block Tree Construction: First Results JF - CoRR Y1 - 2025 U6 - https://doi.org/10.48550/arXiv.2512.23314 VL - abs/2512.23314 SP - 1 EP - 18 ER - TY - CHAP A1 - Clausecker, Robert A1 - Kurpicz, Florian A1 - Palanga, Etienne T1 - Practical Parallel Block Tree Construction T2 - 24th International Symposium on Experimental Algorithms, SEA 2026, Copenhagen, Denmark, June 22-24, 2026 Y1 - 2026 U6 - https://doi.org/10.4230/LIPICS.SEA.2026.13 VL - 371 SP - 13:1 EP - 13:19 ER - TY - JOUR A1 - Demircan, Ayhan A1 - Evlyukhin, Andrey B. A1 - Morgner, Uwe A1 - Melchert, Oliver A1 - Babushkin, Ihar A1 - Zhang, Shihao A1 - Biancalana, Fabio A1 - Mei, Chao A1 - Fan, Jintao A1 - Yang, Peilong A1 - Steinmeyer, Günter A1 - Lalanne, Philippe A1 - Miller, Owen D A1 - Barati Sedeh, Hooman A1 - Litchinitser, Natalia M A1 - Vaičaitis, Virgilijus A1 - Balachninaitė, Ona A1 - Takayama, Osamu A1 - Malureanu, Radu A1 - Lavrinenko, Andrei V A1 - Wetzel, Benjamin A1 - Chaves, Bruno P A1 - Tonello, Alessandro A1 - Couderc, Vincent A1 - Tuz, Vladimir R A1 - Husakou, Anton A1 - Bergé, Luc A1 - Davoine, Xavier A1 - Gremillet, Laurent A1 - Vábe, Jan A1 - Catoire, Fabrice A1 - Skupin, Stefan A1 - Polynkin, Pavel A1 - Barker, Jacob A1 - Palastro, John A1 - Sergeyev, Sergey A1 - Kbashi, Hani A1 - Turitsyn, Sergei K A1 - Menshikov, Evgenii A1 - Franceschini, Paolo A1 - De Angelis, Costantino A1 - Kivshar, Yuri S A1 - [...], A1 - Binkowski, Felix A1 - Betz, Fridtjof A1 - Hammerschmidt, Martin A1 - Schneider, Philipp-Immanuel A1 - Zschiedrich, Lin A1 - Burger, Sven A1 - [...], A1 - Martin, Olivier J. F. A1 - Krause, Pascal A1 - Bande, Annika T1 - Roadmap on 5D Photonics for nano and beyond JF - Nano Futures Y1 - 2026 U6 - https://doi.org/10.1088/2399-1984/ae8631 ER - TY - JOUR A1 - Krishnadas, Anirudh A1 - Palheta, João Marcos Tomaz A1 - Mekonnen, Jonathan Bekele A1 - Parreira, Renato Luis Tame A1 - Flores, Efracio Mamani A1 - Fournier, René A1 - Piotrowski, Maurício Jeomar T1 - Stability of Some Ternary 13-Atom Icosahedral Clusters Assessed with Geometric, Electronic, and Thermodynamic Criteria JF - The Journal of Physical Chemistry A N2 - Superatoms are clusters with physical or chemical properties that bear certain similarities to atoms. We calculated equilibrium geometries and properties using spin-polarized density functional theory (DFT) within the generalized gradient approximation (PBE). We also performed Ab Initio Molecular Dynamics (AIMD) simulations in the NVT ensemble with a Nosé-Hoover thermostat. In addition, Monte Carlo simulations with a potential fitted to DFT were carried out to assess a number of clusters as possible superatoms. The clusters we studied have a chemical composition AB2C10, where A, B, and C are metals, and a structure derived from that of a 13-atom icosahedron. We adopt an operational definition of superatom to assess clusters. According to this definition, superatoms should have (i) a quasispherical equilibrium geometry and (ii) a set of valence orbitals delocalized over the cluster; (iii) they should maintain these two attributes at elevated temperatures (mechanical and thermal stability); and (iv) they should be thermodynamically favored relative to similar clusters. By these criteria, Sn3Y10, CrSn2Zr10, and possibly CrSn2Ti10 can be classified as “superatoms”. We show that cluster stability arises from (i) the intrinsic stability of the C10 fragment; (ii) its interaction energy with the three “dopant atoms”; and (iii) compatible atomic sizes of the three elements. Clusters such as CrSn2Ti10 and CrSn2Zr10 exhibit an optimal balance among these contributions. They have a high effective coordination, an absence of very low vibrational frequencies, a favorable ratio of their elements’ cohesive energy, and exceptional thermal resilience, with melting temperatures that are well above the weighted average of their elements’ bulk melting points. Electronic analysis shows that CrSn2Ti10 and CrSn2Zr10 display discrete density-of-states features and partial electron delocalization, consistent with superatomic behavior. These electronic properties vanish as the clusters approach the melting point and undergo structural distortion. We show the importance of looking at several, often interrelated, properties in assessing possible superatoms. Y1 - 2026 U6 - https://doi.org/10.1021/acs.jpca.6c02945 N1 - Selected as cover article of the journal issue. VL - 130 IS - 30 SP - 5852 EP - 5862 ER - TY - JOUR A1 - Qiao, Lu A1 - Rodrigues Pelá, Ronaldo A1 - Draxl, Claudia T1 - Disentangling Electronic and Lattice Contributions to Transient Absorption in Metal Halide Perovskites: A First-Principles Study of CH3NH3PbBr3 Y1 - 2026 U6 - https://doi.org/10.48550/arXiv.2607.04840 ER - TY - JOUR A1 - Kehrer, Kristina A1 - Conrad, Tim O.F. T1 - A hybrid ABM-PDE framework for real-world infectious disease simulations JF - Applied Mathematical Modelling N2 - This paper presents a hybrid modelling approach that couples an Agent-Based Model (ABM) with a partial differential equation (PDE) model in an epidemic setting to simulate the spatial spread of infectious diseases using a compartmental structure with seven health states. The goal is to reduce the computational complexity of a full-ABM by introducing a coupled ABM-PDE model that offers significantly faster simulations while maintaining comparable accuracy. Our results demonstrate that the hybrid model not only reduces the overall simulation runtime (defined as the number of runs required for stable results multiplied by the duration of a single run) but also achieves smaller errors across both 25% and 100% population samples. The coupling mechanism ensures consistency at the model interface: agents crossing from the ABM into the PDE domain are removed and represented as density contributions, while surplus density in the PDE domain is used to generate agents with plausible trajectories derived from mobile phone data. We evaluate the hybrid model using real-world mobility and infection data for the Berlin-Brandenburg region in Germany, showing that it captures the core epidemiological dynamics while enabling efficient large-scale simulations. These results demonstrate that the proposed ABM-PDE framework provides a robust and computationally efficient alternative to full-scale agent-based simulations, making it suitable for realistic epidemic modelling and scenario analysis. Y1 - 2026 U6 - https://doi.org/10.1016/j.apm.2026.117173 SN - 0307-904X VL - 162 PB - Elsevier BV ER - TY - JOUR A1 - Heyland, Mark A1 - Deppe, Dominik A1 - Gabriele, Matteo A1 - Zierke, Julian N. A1 - Leskovar, Marko A1 - Reisener, Marie-Jacqueline A1 - Rozhko, Yuriy A1 - Ziegeler, Katharina A1 - Damm, Philipp A1 - Reinke, Simon A1 - Stöckle, Ulrich A1 - Duda, Georg N. A1 - Zachow, Stefan A1 - Trepczynski, Adam T1 - Callus formation during healing is guided by local strain: a retrospective clinical observation JF - BMC Musculoskeletal Disorders N2 - Abstract Background Clinically, fracture healing is typically monitored though serial radiographs. Specifically, callus development (growth and mineralization) is an indicator of healing and associated with local mechanical strain. However, a sustainable relationship between mechanical conditions and the respective healing progress has not been shown so far. Material and methods One hundred sixty-six patients with extra-articular lower-limb fractures treated by osteosynthesis plates or intramedullary nails were included. Callus formation (visible area in X-ray relative to bone shaft) and quality (image intensity relative to the cortex) were measured by consecutive X-ray analyses as well as the modified Radiographic Union-Score for Tibia (mRUST) during follow-up. Corresponding load- and fixation-matched finite element analysis (FEA) modelling was developed for tibia or femur loading as well as plate or intramedullary nail fixation. Mechanical strains (medially, laterally, dorsally, anteriorly) were evaluated from FEA and compared to the progress in X-ray callus formation and quality to perform a correlation analysis between observed callus formation and simulated local mechanical strains. Results For femoral fractures, callus size was largest dorso-medially (1.41 ± 1.57 cm 2 /cm and 1.18 ± 1.11 cm 2 /cm at 180 ± 45 days post-surgery) while largest callus formations were found laterally in tibial fractures (0.75 ± 0.49 cm 2 /cm at 365 ± 45 days post-surgery). These locations of maximal callus size in femur and tibia matched the locations of extreme principal strains from FEA. In femur, callus density increased steadily and exceeded cortex density at 365 ± 45 days post-surgery. For tibia, no such clear trend was observable. While initially showing a similar increase in callus bridging score mRUST, increase over 2 years was 48% higher for the tibial fractures compared to femoral fractures. While principal strains correspond to increases in early callus formation in both femur and tibia (Kendall-Tau-b: p  = 0.021 for volumetric strain at 90 ± 45 days post-surgery), shear strains are consistently associated with less callus formation (Kendall-Tau-b: p  = 0.048 for volumetric/shear strain associated with callus size*density at 365 ± 45 days post-surgery). Conclusions Callus formation during bone healing may be associated with local mechanical strain in lower limb fractures within a clinically relevant cohort including different fracture locations and fixation types. Shear strain at the fracture site appeared to be associated with reduced quality callus formation, whereas principal strain was observed to correlate with increased early callus formation. The presented methodology may have potential as a predictor of healing processes and could help identify mechanically challenging fracture fixation settings. Level of evidence II. Trial registration Ethical approval was obtained from the local ethics board to this retrospective study design (EA4/099/24). Y1 - 2026 U6 - https://doi.org/10.1186/s12891-026-10118-2 SN - 1471-2474 VL - 27 IS - 1 PB - Springer Science and Business Media LLC ER - TY - JOUR A1 - Jianning, Li A1 - Sengupta, Agniva A1 - Zachow, Stefan T1 - Uncertainty estimation and probabilistic skull shape reconstruction using bayesian neural networks JF - Scientific Reports N2 - 3D shape reconstruction is an active area of research and a fundamental problem in computer vision, with growing applications in the medical domain, where it enables the recovery of missing or fine anatomical structures. Numerous approaches have been proposed for medical shape reconstruction, emphasizing accurate and anatomically plausible reconstructions. However, 3D shape reconstruction remains an inherently ill-posed problem, meaning (1) both neural network-based methods and conventional shape modeling approaches naturally introduce uncertainty in their predictions, and (2) multiple anatomically plausible reconstructions exist for a given partial or low-resolution input. While the uncertainty aspects have been widely explored in general computer vision, they remain relatively under-explored in the context of medical shape reconstruction. In this paper, we developed a 3D Bayesian U-Net and investigated its use for uncertainty estimation and probabilistic reconstructions across three key tasks: cranial reconstruction, facial reconstruction, and skull shape super-resolution. Our findings show that the Bayesian model is able to produce a range of anatomically plausible skull reconstructions while capturing natural skull variations arising from the learned weight uncertainty. Notably, these variations are primarily expressed through differences in bone thickness, which aligns with anatomical expectations, particularly relevant in real-world applications like cranial implant design. Additionally, we propose a principled framework to study the relationship between weight uncertainty and reconstruction uncertainty by analyzing the learned posterior distribution of the weights, demonstrating that our Bayesian U-Net achieves comparable reconstruction performance to a deterministic U-Net baseline while providing reliable uncertainty estimates. Our study also reveals a clear cross-task uncertainty pattern, where tasks with stronger structural constraints, like super-resolution, yield lower predictive uncertainty, while less constrained tasks, like facial reconstruction, result in higher uncertainty. Refer to the project page for more visual results https://git.zib.de/jli/uncertainty-aware-skull-reconstruction/. Y1 - 2026 U6 - https://doi.org/10.1038/s41598-026-54679-7 VL - 16 ER - TY - JOUR A1 - Blaskovic, Filip A1 - Klus, Stefan A1 - Conrad, Tim A1 - Djurdjevac Conrad, Natasa T1 - Spectral clustering of time-evolving networks using spatio-temporal random walks N2 - Temporal (or time-evolving) networks provide a natural framework for modeling complex systems with time-dependent interactions, where understanding the evolution of community structures is a central challenge. While random walk-based approaches to community detection in static networks are well established through the spectral analysis of associated transfer operators, extending these ideas to temporal networks is nontrivial due to the inherent time-dependence of the underlying dynamics. In this work, we develop a general framework for community detection in temporal networks that is based on multi-view canonical correlation analysis (mCCA). We show that the proposed formulation admits a spectral characterization via a time-reversible random walk on an augmented space-time network, providing a clear dynamical interpretation of temporal communities as metastable structures of the process. Furthermore, we analyze key spectral properties of the resulting transfer operators and the interplay between spatial and temporal effects, which allows us to distinguish between structural features and artifacts induced by the snapshot coupling. Finally, we derive a reduced-order model, which preserves the essential spectral properties while significantly improving computational efficiency. We show that the proposed approach effectively detects communities in temporal networks and captures their evolution. Y1 - 2026 ER - TY - JOUR A1 - Koch, Thorsten A1 - Bernal Neira, David E. A1 - Chen, Ying A1 - Cortiana, Giorgio A1 - Egger, Daniel J. A1 - Heese, Raoul A1 - Hegade, Narendra N. A1 - Gomez Cadavid, Alejandro A1 - Huang, Rhea A1 - Itoko, Toshinari A1 - Kleinert, Thomas A1 - Maciel Xavier, Pedro A1 - Mohseni, Naeimeh A1 - Montanez-Barrera, Jhon A. A1 - Nakano, Koji A1 - Nannicini, Giacomo A1 - O’Meara, Corey A1 - Pauckert, Justin A1 - Proissl, Manuel A1 - Ramesh, Anurag A1 - Schicker, Maximilian A1 - Shimada, Noriaki A1 - Takeori, Mitsuharu A1 - Valls, Víctor A1 - Van Bulck, David A1 - Woerner, Stefan A1 - Zoufal, Christa T1 - The Quantum Optimization Benchmarking Library JF - Nature Computational Science Y1 - 2026 U6 - https://doi.org/10.1038/s43588-026-00991-1 SN - 2662-8457 VL - 6 IS - 6 SP - 653 EP - 671 PB - Springer Science and Business Media LLC ER - TY - JOUR A1 - Gaskin, Thomas A1 - Bankowski, Anastasia A1 - Winkelmann, Stefanie T1 - Towards a Unified Theory of Hybrid Neural Models N2 - Integrating neural networks into mechanistic, equation-based models is a field of growing scientific interest, yet it lacks consistent terminology and a unified mathematical framework. We systematise existing approaches and establish new connections between hybrid modelling, differential equations theory, and deep learning. We clarify the roles of inference, prediction, and generalisation in hybrid models, showing how neural components can capture mathematical structures within and across datasets, and we connect these questions to structural identifiability theory. We further interpret hybrid models through a manifold-learning lens, demonstrating how parametric spaces of missing information can be learned, thereby providing an alternative approach to uncertainty quantification. Y1 - 2026 U6 - https://doi.org/10.21203/rs.3.rs-8661295/v1 ER - TY - JOUR A1 - Wehlitz, Nathalie A1 - Scherzer, Richard A1 - Hartmann, Carsten A1 - Winkelmann, Stefanie T1 - Energetic characterisation of transient clustering dynamics in aggregation-diffusion systems N2 - We investigate transient clustering dynamics in nonlocal aggregation-diffusion systems from an energetic perspective. Starting from a stochastic interacting particle system, we study the associated macroscopic McKean-Vlasov equation on the torus and exploit its Wasserstein gradient-flow structure to analyse the thermodynamic competition between interaction-driven aggregation and entropy-driven diffusion. Through numerical experiments for locally attractive interaction kernels, we identify alternating aggregation- and diffusion-dominated transient regimes along trajectories converging to fixed equilibria. These dynamics can be interpreted as a form of non-monotone clustering behaviour. Moreover, we demonstrate that clustering observables, such as the density peak height, are only partially coupled to the underlying energetic mechanisms and therefore do not uniquely characterise the relevant macroscopic transport dynamics. Our results highlight the role of the variational structure not only for equilibrium analysis, but also as a framework for understanding transient clustering phenomena in interacting particle systems. Y1 - 2026 ER - TY - JOUR A1 - Márton, Lorinc A1 - Winkelmann, Stefanie A1 - del Razo, Mauricio A1 - Djurdjevac Conrad, Natasa T1 - Opinion Dynamics over Migration Networks N2 - Opinions play a crucial role in shaping collective phenomena such as political polarization, cultural integration and demographic change. By continuously changing social environments in which opinions evolve, human migration serves as an important driver of collective opinion formation. While migration and opinion dynamics have both been extensively studied, the few existing models that couple the two are primarily deterministic and therefore cannot capture demographic fluctuations, finite-size effects or stochastic transitions between emergent collective states. To address this limitation, we introduce a unifying stochastic framework for opinion dynamics over migration networks that couples local opinion transitions, demographic processes and migration between communities. The dynamics are formulated through a spatio--temporal master equation, which provides a probabilistic description of the underlying population process. From this microscopic representation, we derive deterministic mean-field equations governing the co-evolution of community sizes and opinion compositions, thereby linking agent-level interactions to macroscopic population behavior. Using two representative case studies, we demonstrate how stochasticity and migration can qualitatively change the emergent dynamics and collective outcomes, including the emergence of consensus, polarization and the stabilization of oscillatory opinion dynamics. These examples highlight the rich interplay between social interactions, demographic change and migration in deterministic and stochastic settings, and they demonstrate that migration should be viewed as an integral component of collective opinion formation rather than only an external demographic process. Y1 - 2026 ER - TY - JOUR A1 - Nagel, Sören A1 - Winkelmann, Stefanie A1 - Koltai, Peter A1 - Djurdjevac Conrad, Natasa A1 - Lücke, Marvin T1 - Collective variables for homophily-driven network rewiring dynamics N2 - Stochastic network rewiring processes, in which edges dynamically rewire based on fixed node attributes, are widely used in applications ranging from social dynamics to neuroscience and form an important component of adaptive network modelling. In this paper, we identify low-dimensional collective variables (CVs) that capture the essential macroscopic behavior of such time-evolving networks and enable reduced-order descriptions of their dynamics. To this end, we apply the data-driven transition manifold approach to homophily-driven rewiring models, in which edges preferentially connect nodes with similar attributes. For two representative models, we find that the optimal CV is a consensus measure quantifying the fraction of edges whose incident nodes differ by less than a certain threshold. Building on the learned CV, we construct reduced macroscopic models using a data-driven approach based on sparse regression and through an analytical derivation using graphons. The latter yields a closed-form evolution equation for the consensus measure and analytically validates the identified CV. Y1 - 2026 ER - TY - THES A1 - Martínez Cisneros, Alonso T1 - Reducing Polarization in Agent-Based Models of Opinion Dynamics using Optimal Control and Reinforcement Learning Y1 - 2026 ER - TY - THES A1 - Kostré, Margarita T1 - Topics in Particle-Based Simulation: Multiscale Models and Optimization Methods N2 - Particle-based models are crucial for capturing stochastic dynamics in a diverse range of fields, including biochemistry, social dynamics, and optimization. These systems often exhibit behavior across multiple spatial or temporal scales, requiring methods that can adapt to multiscales. However, particle-based models lack a rigorous theoretical foundation due to their complexity. In biochemistry, a key challenge in particle-based modeling is developing simulations that operate consistently across the microscopic and macroscopic scale while remaining computationally feasible. This thesis establishes a mathematically consistent coupling between particle-based simulations and reactiondiffusion partial differential equations (PDEs). To this end, we derive the Kurtz representation of the reaction-diffusion master equation (RDME) and we extend the chemical diffusion master equation (CDME) to an open setting. Then, we connect thermodynamic properties across scales and incorporate cross-scale interactions by matching the mean-fields of the particle-based models (RDME and CDME) with the PDE-based dynamics. Two new hybrid numerical schemes are developed to efficiently couple particle-based domains with a concentration reservoir. Like in simulation, a key challenge in particle-based optimization is adapting effectively to multiscale landscapes. This thesis develops a novel parameter selection framework for the Particle Swarm Optimization (PSO) algorithm to navigate a multiscale loss landscape. We define new parameter regions based on the moments of particles, which characterize the exploration and exploitation of the optimization algorithm, along with suitable measures for exploration and exploitation. Building on the parameter selection framework, we show that our PSO framework identifies a greater variety of solutions when inferring cultural diffusion of Roman influence. Y1 - 2026 UR - http://dx.doi.org/10.17169/refubium-52223 ER - TY - JOUR A1 - Giliberti, Gemma A1 - Farias-Basulto, Guillermo A1 - Jäger, Klaus A1 - Mehlhop, Thede A1 - Kaufmann, Christian A. A1 - Di Carlo, Aldo T1 - Assessment and Optimization of 2T Perovskite/CIGS Tandems via Data-Driven and Optoelectronic Modelling JF - Nano-Micro Lett. Y1 - 2026 U6 - https://doi.org/10.1007/s40820-026-02141-8 VL - 18 SP - 312 ER - TY - JOUR A1 - Krishnadas, Anirudh A1 - Kansal, Jatin A1 - Charron, Nicholas A1 - Ragyanszki, Anita T1 - Spectral Quantum Chemistry and Infrared Resonance Library for Data-Driven Molecular Spectroscopy JF - Scientific Data N2 - Infrared (IR) spectroscopy is a fundamental tool for molecular identification and characterization, yet comprehensive IR spectral databases remain limited, particularly for small organic molecules with well-defined theoretical baselines. Here we introduce SQuIRL, the Spectral Quantum Chemistry and Infrared Resonance Library, a collection of computed IR spectra for 133,885 organic molecules. Each entry provides vibrational frequencies and intensities with near-benchmark accuracy, thereby extending the QM9 dataset by incorporating vibrational fingerprints alongside its structural and electronic descriptors. The resulting dataset enables data-driven spectrum prediction, machine-learning model training, and automated molecular identification. SQuIRL establishes a new foundation for high-fidelity, quantum-accurate infrared spectroscopy in computational chemistry. Distributed in structured HDF5 format with an accessible API, it integrates seamlessly into AI-based spectroscopy workflows and molecular discovery pipelines, offering a widely accessible resource for data-driven approaches in vibrational spectroscopy. Y1 - 2026 U6 - https://doi.org/10.1038/s41597-026-07240-0 VL - 13 PB - Springer Nature ER - TY - GEN A1 - Hölter, Arne A1 - Schubert, Yannick A1 - Stein, Lewin A1 - Sroka, Mario A1 - Lemke, Mathias T1 - CRUNA – An Open Source Finite-Difference Time-Domain Optimization Solver based on Adjoint Gradients T2 - Proceedings of the 12th Convention of the European Acoustics Association N2 - Wave-based simulation methods are well suited for analyzing acoustic problems in which wave phenomena such as diffraction and scattering play a dominant role. Among these, the finite-difference time-domain (FDTD) method is a robust and versatile approach. This work presents CRUNA, an open-source 3D FDTD solver with an integrated optimization framework based on adjoint gradient computation. While the modular codebase addresses a wide variety of flow-related problems, this contribution focuses on the acoustic and aeroacoustic components of the solver and their application to wave propagation and optimization tasks. The solver supports several sets of governing equations, ranging from linearized acoustic formulations to more comprehensive flow models. It provides explicit time-stepping schemes, as well as explicit and implicit spatial discretization methods. CRUNA is highly parallelized to enable fast execution in high-performance computing environments. Non-reflective characteristic boundary conditions are implemented, and immersed boundary methods based on effective volume and flow resistivity enable an accurate representation of complex geometries and impedance boundaries. Representative examples demonstrate the framework, including aeroacoustic simulations, sound source localization and interpolation, and pinna-related transfer function computations. By providing CRUNA as open-source software, this work aims to support transparency, reproducibility, and further development within the computational acoustics community. Y1 - 2026 N1 - accepted for publication ER - TY - JOUR A1 - Hölter, Arne A1 - Lemke, Mathias A1 - Weinzierl, Stefan A1 - Stein, Lewin T1 - A Tutorial on Non-Reflective Characteristic Boundary Conditions for Direct and Adjoint Time-Domain Acoustic Simulations JF - Journal of Theoretical and Computational Acoustics N2 - Accurate free-field acoustic simulations require non-reflective boundary conditions to suppress spu- rious reflections at the computational domain boundaries. Although several characteristic-based formulations for direct (forward) simulations have been proposed in recent decades, adjoint formu- lations of such characteristic-based boundary conditions (CBCs) have received limited attention in the literature and lack a comprehensive analysis. This paper presents the derivation and evaluation of adjoint CBCs complementing existing direct CBCs. Both direct and adjoint CBCs are applied to the nonlinear Euler equations and linear acoustic equations in time-domain simulations. The resulting boundary treatments are compared in terms of accuracy, reflection behavior, and direct-adjoint consistency. The CBCs are implemented using a single-point approach with both static and dynamic wave- vector estimates and are optionally combined with a sponge layer. Over the evaluated frequency range of 350 – 4000 Hz, spurious reflections are effectively attenuated in both direct and adjoint simulations. The adjoint CBCs show reflection behavior consistent with their direct counterparts, demonstrating that the proposed formulation provides a consistent open-boundary treatment for adjoint time-domain acoustics. Y1 - 2026 N1 - under review ER - TY - CHAP A1 - Buchholz, Annika A1 - Riedmüller, Stephanie A1 - Passage, Matthew A1 - Zittel, Janina T1 - Benchmarking Realistic Synthetic Instances Against a Large-Scale District Heating Network: A Multi-Objective Optimization Study for Berlin T2 - ECOS 2026 N2 - Decarbonizing urban energy systems requires optimization approaches capable of handling the operational complexity of large-scale district heating networks. However, existing studies typically focus on a single, real-world network, which makes results difficult to compare and limits the transferability of insights to other systems. To overcome these challenges and enable a more systematic evaluation, realistic synthetic instances play an important role: they provide controlled, reproducible environments for testing optimization algorithms independent of a specific case study while still capturing essential structural and temporal characteristics of real systems. When designed carefully, such instances enable systematic benchmarking, facilitate methodological development, and support comparative studies across different algorithms and modeling choices. In this work, we generate a suite of large-scale synthetic instances for multi-objective optimization of district heating systems. The instances are openly available in the form of the underlying network topology in a JSON format and also as a mixed integer program (MIP) as MPS files to enable modeling and algorithmic benchmarking. They are constructed using a transparent procedure that allows to recreate or extend the given instances or apply the same procedure to other network based problems. We apply this methodology to Berlin’s district heating network, the most complex in Western Europe, formulating a tri-objective mixed-integer model focused on unit commitment over a horizon of up to 25 years with a 4-hour temporal resolution. The corresponding real-world instance serves as a reference point to demonstrate that the generated datasets reflect key behaviors of actual district heating operations. A computational study provides a detailed comparison between the synthetic instances and the real-world Berlin data, showing under which conditions the generated instances reproduce realistic optimization characteristics. Furthermore, we investigate which features make the resulting models computationally challenging. The findings highlight how well-designed synthetic instances can support robust benchmarking practices and enable meaningful assessment of (multi-objective) optimization methods for large-scale district heating systems. Y1 - 2026 ER - TY - JOUR A1 - Kobeleva, Elizaveta A1 - Chewle, Surahit A1 - Horch, Marius A1 - Weber, Marcus T1 - A Generalized NMF-Based Method for Analyzing Time-Resolved Spectroscopic Data JF - The Journal of Physical Chemistry A N2 - Time-resolved spectroscopy is a widely used tool for the investigation of physical and chemical processes. Analysis of the results is often challenging due to the inherent complexity of the data, encoding the chemical nature and time evolution of multiple species involved in the reaction. Many existing analytical methods are unsatisfactory as they introduce bias by relying on unjustified mathematical or mechanistic assumptions about the studied process. Here, we introduce a generalized analytical strategy based on non-negative matrix factorization. The methodology builds on a bottom-up model-free approach, in which physically grounded mathematical constraints can be introduced by active choice, allowing for an unbiased analysis of complex time series of spectroscopic data. The strength of this strategy is demonstrated by successful deconvolution of synthetic data mimicking different types of chemical reactions and typical challenges encountered in time-resolved Raman spectroscopy. Y1 - 2026 U6 - https://doi.org/10.1021/acs.jpca.6c00974 VL - 130 IS - 20 SP - 3959 EP - 3968 ER - TY - CHAP A1 - Wies, Tobias A1 - Kharma, Sami A1 - Meuser, Tobias A1 - Scheuermann, Björn T1 - Accurate Synthetic Tasks for Scientific Workflow Benchmarks T2 - CCGrid 2026 Y1 - 2026 N1 - Doctoral Symposium paper ER - TY - CHAP A1 - Kharma, Sami A1 - Wies, Tobias A1 - Scheuermann, Björn A1 - Schintke, Florian T1 - Topology-Aware Task and Data Placement for Distributed Scientific Workflows T2 - CCGrid 2026 Y1 - 2026 N1 - Doctoral Symposium paper ER - TY - CHAP A1 - Witzke, Joel A1 - Lößer, Ansgar A1 - Bader, Jonathan A1 - Lehmann, Fabian A1 - Scheuermann, Björn A1 - Schintke, Florian T1 - Optimizing workflow execution by cost-effective I/O monitoring, bottleneck analysis, and proactive resource assignment T2 - Workflow systems for large-scale scientific data analysis Y1 - 2026 SN - 978-3-98781-067-1 U6 - https://doi.org/10.14279/depositonce-25832 SP - 455 EP - 493 PB - Berlin Universities Publishing ER - TY - JOUR A1 - Mansour, Ahmed A1 - Demerdash, Yasmin A1 - Zhou, Xiao-Ran A1 - Leidel, Antonia A1 - Reidelbach, Marco A1 - Langenbach, Christian A1 - Castro, Leyla Jael A1 - Watson, Juliane A1 - Vishen, Neelam A1 - Windeck, Jürgen T1 - DMP Evaluation Criteria - focus on project-lifecycle, research discipline and AI-assisted evaluations Y1 - 2026 U6 - https://doi.org/10.5281/zenodo.19630859 ER - TY - JOUR A1 - Petkovic, Milena A1 - Zakiyeva, Nazgul A1 - Chen, Ying A1 - Hadjidimitrou, Natalia Selini A1 - Xu, Xiuqin A1 - Koch, Thorsten A1 - Zittel, Janina T1 - An automated model switching framework for efficient entry nomination forecasting in natural gas transmission networks JF - Energy Systems N2 - As a result of the legislation for gas markets introduced by the European Union in 2005, separate independent companies have to conduct the transport and trading of natural gas. The current gas market in Germany, which has a market value of more than 54 billion USD, consists of Transmission System Operators (TSO), network users, and traders. Traders can nominate a certain amount of gas anytime and anywhere in the network. Such unrestricted access for the traders creates a free market, while on the other hand, it increases the uncertainty in the supply management and gas network operations. Some customers’ behaviors may cause abrupt structural changes in gas flow time series. For this reason, it is challenging for the TSOs to accurately predict the multiple hours-ahead gas nominations. Our study aims to investigate the customers’ behavior in giving the nominations in advance for particular hours and to predict the final gas nominations up to 8 hours-ahead as precisely as possible. We propose an Automated Model Switching framework (AMS) for an accurate, robust, and efficient multi-step ahead prediction of entry point nominations in gas transmission networks.The results demonstrate that AMS achieves excellent performance, outperforming the best individual state-of-the-art models for the vast majority of the test cases while keeping the calculations as simple as possible. Y1 - 2026 U6 - https://doi.org/10.1007/s12667-026-00802-6 ER - TY - JOUR A1 - Chen, Liuni A1 - Feldstein, Hannah A1 - Jia, Zian A1 - Hu, Chenhao A1 - Chen, Hongshun A1 - Geng, Yang A1 - Peterman, Emily M. A1 - Slebodnick, Carla A1 - Speiser, Daniel I. A1 - Baum, Daniel A1 - Kolle, Mathias A1 - Li, Ling T1 - A possible biomineralized light-guiding structure in the porous ossicular skeletons of the sea star Protoreaster nodosus JF - PNAS N2 - Biomineralized structures produced by living organisms are widely recognized for their exceptional mechanical performance, yet their potential optical roles are relatively less explored. Here, we demonstrate that within the calcitic ossicle-based skeleton of the sea star Protoreaster nodosus, where each ossicle represents a discrete skeletal element, one specialized ossicle, known as the terminal plate, contains a radially arranged array of light-guiding structures (LGSs). These LGSs exhibit an elongated, cone-like geometry (~250 μm in length) and are embedded within the porous stereom, a characteristic meshwork architecture of echinoderms analogous to open-cell cellular solids and composed of magnesium-containing single-crystalline calcite. Optical experiments demonstrate that, unlike other skeletal elements, the terminal plate can transmit and focus light into an internal cavity via the LGS array. Combined optical analyses using ray-tracing and finite-difference time-domain (FDTD) simulations reveal that each LGS transmits ca. 70% of incident light at normal incidence and concentrates it up to 2.8-fold at its exiting surface. Furthermore, when acting collectively as the LGS array within the terminal plate, the LGSs capture light over a broad field of view (~120°), resulting in an integrated transmitted intensity that is six- to eight-fold greater than the incoming intensity perceived by a single LGS. Although the biological function of this optical capability remains uncertain, this natural porous structure demonstrates that cellular solids can integrate efficient light-guiding behavior while enhancing mechanical properties (i.e., threefold increase in stiffness compared with random stereom), offering new design insights for lightweight, multifunctional structures. Y1 - 2026 ER - TY - JOUR A1 - Roth, Sarah A1 - Jäger, Sven A1 - Lindner, Niels A1 - Schöbel, Anita T1 - Optimizing Travel Time and Regenerative Energy for Periodic Timetables N2 - Regenerating braking energy is one major pathway to make rail traffic energy-efficient. It is therefore desirable to design timetables that exploit this feature. However, timetables that allow to regenerate energy are often bad for the passengers. We hence formulate and analyze a bicriteria optimization problem (PESP-Passenger-Energy) to find periodic railway timetables that maximize the regenerated energy in terms of the brake-traction overlap time and minimize the travel time of the passengers. Our model extends the Periodic Event Scheduling Problem (PESP) and offers a rich combinatorial theory. We investigate its computational complexity on one-station networks, building on matchings and Hamiltonian paths. Besides showing its NP-hardness even for a single objective, we identify several polynomial-time solvable special cases. Finally, we provide two case studies, underlining the practicability of our model, and analyzing the Pareto front. Y1 - 2026 ER - TY - CHAP A1 - Zittel, Janina A1 - Buchholz, Annika A1 - Bussieck, Michael A1 - Fiand, Frederik A1 - Koch, Thorsten A1 - Lindner, Niels A1 - Mehl, Lukas A1 - Wetzel, Manuel T1 - Computational acceleration strategies for large-scale energy system optimization: a comparative study of GPU-accelerated and distributed-memory solvers T2 - ECOS 2026 N2 - Energy system optimization models are increasing in scope and resolution, integrating detailed technology representations, sector coupling, and multiple scenarios reflecting uncertainty in future energy demand. These advances yield large and challenging linear programs whose efficient solution remains a bottleneck in practical energy system analyses. For a long time, the standard way to address such problems has relied on shared-memory interior-point methods (IPM), which combine robustness and accuracy but face scalability limits as model instance size grows. Specialized solver architectures are beginning to change this picture. Two promising directions have emerged: (i) GPU-accelerated first-order methods (FOM) such as the primal-dual linear programming approach; and (ii) distributed-memory IPM, exemplified by the open-source solver PIPS-IPM++, which can exploit block structure that arises in many energy system models and achieves high parallelism on highperformance computing systems. These developments open new opportunities for large-scale optimization in energy system analysis. This paper presents a computational study comparing these solver classes on a diverse test set of largescale linear programs arising from energy system analysis, including scenario-based formulations derived from stochastic programming. We investigate how parallelization strategies affect solution time and numerical accuracy. The results illustrate that distributed-memory IPM can leverage problem structure to deliver substantial speed-ups on specific problems with block-angular structures. GPU-accelerated FOMs demonstrate strong scalability but may yield solutions with higher relative infeasibilities, which, depending on the use case and model uncertainty, can still be acceptable. Overall, our findings indicate that recent algorithmic and hardware advances substantially broaden the computational toolbox available to the energy system optimization community. Each solver class exhibits distinct advantages: shared-memory IPMs remain a powerful tool for reliably obtaining high-accuracy solutions; distributed-memory IPMs can extend scalability to hundreds of cores for certain structured models, enabling faster time-to-solution; and GPU-based FOM can deliver fast solutions when such lower accuracy levels are appropriate. Together, they help make high-resolution, multi-scenario energy system optimization models tractable across a broader range of problem sizes and computing environments. Y1 - 2026 ER - TY - JOUR A1 - Zimper, Sebastian A1 - Djurdjevac Conrad, Natasa A1 - Cornalba, Federico A1 - Djurdjevac, Ana T1 - Clustering in co-evolving opinion dynamics: reduced SPDE models N2 - Clustering is a fundamental collective phenomenon in agent-based models (ABMs) of opinion dynamics. To study clustering in systems with co-evolving social and opinion variables, we derive stochastic partial differential equation (SPDE) models that describe the evolution of clusters on a reduced state space. We consider two settings: one in which opinions do not affect social interactions, and another one in which a feedback mechanism couples the two. Our approach extends reduced PDE modelling to a stochastic framework, which is essential for capturing long-term cluster behaviour. Numerical experiments demonstrate that the proposed reduced SPDEs substantially decrease computational cost compared to full-state SPDE models, such as the Dean–Kawasaki equation, while still accurately reproducing the clustering behaviour of the underlying ABM. As a result, these reduced models provide an efficient tool for studying systems with large populations, including those arising in the analysis of real-world data: in particular, we provide an application related to the large-scale General Social Survey (GSS), which comprises opinion and social data of the US population since 1972. Y1 - 2026 ER - TY - JOUR A1 - Secker, Christopher A1 - Secker, Philipp A1 - Yergöz, Fatih A1 - Celik, M. Özgür A1 - Chewle, Surahit A1 - Le, Maria Phuong Nga A1 - Masoud, Mustafa A1 - Christgau, Steffen A1 - Weber, Marcus A1 - Gorgulla, Christoph A1 - Nigam, AkshatKumar A1 - Pollice, Robert A1 - Schütte, Christof A1 - Fackeldey, Konstantin T1 - Evolutionary exploration of drug-like chemical space utilizing generative AI and virtual screening JF - bioRxiv Y1 - 2026 U6 - https://doi.org/10.64898/2026.03.26.714527 ER - TY - JOUR A1 - Coomber, Celvic A1 - Kresse, Jakob J. A1 - Chewle, Surahit A1 - Weber, Marcus A1 - Schütte, Christof A1 - Sunkara, Vikram T1 - Analyzing the Molecular Effects of Endomorphin-2 Degradation on Stabilizing Interactions at the μ-Opioid Receptor JF - Receptors Y1 - 2026 U6 - https://doi.org/10.3390/receptors5020015 VL - 5 IS - 2 ER - TY - JOUR A1 - Temp, Julia A1 - Celik, Melih Özgür A1 - Zabarylo, Urszula A1 - Dominika, Labuz A1 - Raum, Kay A1 - Zachow, Stefan A1 - Machelska, Halina T1 - Whole-body vibration decreases pain and cartilage degeneration in male and female mice with osteoarthritis JF - Pain Reports Y1 - 2026 U6 - https://doi.org/10.1097/PR9.0000000000001442 VL - 11 IS - 3 SP - e1442 PB - LIPPINCOTT WILLIAMS & WILKINS ER - TY - JOUR A1 - Pedersen, Jaap A1 - Lindner, Niels A1 - Rehfeldt, Daniel A1 - Koch, Thorsten T1 - Integrated Wind Farm Design: Optimizing Turbine Placement and Cable Routing with Wake Effects JF - OR Spectrum N2 - An accelerated deployment of renewable energy sources is crucial for a successful transformation of the current energy system, with wind energy playing a key role in this transition. This study addresses the integrated wind farm layout and cable routing problem, a challenging nonlinear optimization problem. We model this problem as an extended version of the quota Steiner tree problem (QSTP), optimizing turbine placement and network connectivity simultaneously to meet specified expansion targets. Our proposed approach accounts for the wake effect - a region of reduced wind speed induced by each installed turbine - and enforces minimum spacing between turbines. We introduce an exact solution framework in terms of the novel quota Steiner tree problem with interference (QSTPI). By leveraging an interference-based splitting strategy, we develop an advanced solver capable of tackling large-scale problem instances. The presented approach outperforms generic state-of-the-art mixed integer programming solvers on our dataset by up to two orders of magnitude. Further, we present a hop-constrained variant of the QSTPI to handle cable capacities in the context of radial topologies. Moreover, we demonstrate that our integrated method significantly reduces the costs in contrast to a sequential approach. Thus, we provide a planning tool that enhances existing planning methodologies for supporting a faster and cost-efficient expansion of wind energy. Y1 - 2026 U6 - https://doi.org/10.1007/s00291-026-00862-1 ER - TY - GEN A1 - Buchholz, Annika A1 - Khebouri, Imene A1 - Vu, Thi Huong A1 - Koch, Thorsten A1 - Kunt, Tim A1 - Peters-Kottig, Wolfgang A1 - Stompor, Tomasz A1 - Zittel, Janina T1 - Detecting and classifying publications based on their abstracts with LLM embeddings and multi-label classifiers T3 - ZIB-Report - 26-04 Y1 - 2026 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-103273 SN - 1438-0064 ER - TY - THES A1 - Amiranashvili, Tamaz T1 - Universal and Expressive Statistical Shape Models for Anatomical Structures N2 - Form and function of anatomical structures are intimately linked. Pathological changes in form can be associated with the loss of function. For example, diseases often cause characteristic shape changes, making shape a sensitive structural biomarker for medical diagnosis. If the link between form and function is causal, correcting a pathological shape can even restore the healthy function of an organ. Accurate shape reconstruction is then crucial for effective, patient-specific treatment planning. This demonstrates the importance of shape in clinical interventions and its potential to improve overall patient outcomes. Statistical shape models are computational methods that capture shape variations in a given population and enable precise shape analysis and generation. We focus on two key properties of a good statistical shape model. First, it should be easy to construct, and second, it should accurately represent the underlying shape distribution. Established existing approaches can only be constructed from surfaces with pre-defined dense correspondence. Such correspondence is tedious to obtain, can introduce undesired biases, and prevents training on partial or sparse observations. While correspondence-free methods exist, they struggle to accurately capture shape distributions with intricate details and large variations. In this thesis, we develop shape models that simplify training and improve accuracy over state-of-the-art. To achieve these goals, we build on approximately diffeomorphic neural deformations and implicit neural representations. First, our proposed methods are trainable on correspondence-free surfaces and even partial segmentations with large slice distances. This makes them universal since they can be trained on heterogeneous data, enabling scalability to large datasets and avoiding potential biases of pre-defined correspondence. Second, our methods are highly expressive, accurately capturing intricate shape details in complex distributions. We evaluate effectiveness of our models on multiple anatomical structures, outperforming established baselines in both generative and discriminative settings. Y1 - 2025 UR - https://mediatum.ub.tum.de/doc/1776778/34rd9v23igjb2ahatohz1rwdx.phd_thesis_Amiranashvili.pdf UR - https://mediatum.ub.tum.de/?id=1776778 ER - TY - JOUR A1 - Schaible, Jonas A1 - Özdemir, Asena Karolin A1 - Debus, Charlotte A1 - Burger, Sven A1 - Streit, Achim A1 - Becker, Christiane A1 - Jäger, Klaus A1 - Götz, Markus T1 - Inverse Design of Optical Multilayer Thin Films using Robust Masked Diffusion Models JF - ArXiV Y1 - 2026 U6 - https://doi.org/10.48550/arXiv.2604.01106 SP - arXiv:2604.01106 ER - TY - GEN A1 - Andrés Arcones, Daniel A1 - Paul, Aeneas A1 - Weiser, Martin A1 - Sanio, David A1 - Mark, Peter A1 - Unger, Jörg F. T1 - Bayesian Tendon Break Localization under Model Uncertainty Using Distributed Fiber Optic Sensors Authors/Creators N2 - Implementation, data and results of the paper "Bayesian Tendon Break Localization under Model Uncertainty Using Distributed Fiber Optic Sensors" Y1 - 2006 U6 - https://doi.org/10.5281/zenodo.18713386 ER - TY - JOUR A1 - Andrés Arcones, Daniel A1 - Paul, Aeneas A1 - Weiser, Martin A1 - Sanio, David A1 - Mark, Peter A1 - Unger, Jörg F. T1 - Bayesian Tendon Breakage Localization under Model Uncertainty Using Distributed Fiber Optic Sensors N2 - This study develops a Bayesian, uncertainty-aware framework for tendon breakage localization in pre-stressed concrete members using high-resolution data from distributed fiber-optic sensors (DFOS). DFOS enable full-field monitoring of strain changes on the surface of pre-stressed concrete members due to such failure. A finite element model (FEM) of an experimental tendon-breakage test is constructed, and model parameters are calibrated probabilistically against DFOS measurements. To capture model-form uncertainty (MFU), stochastic perturbations are embedded directly into material parameters, enabling the joint inference of physical properties and MFU within a unified probabilistic framework. Gaussian Process surrogates are employed to efficiently emulate the nonlinear FEM response, supporting computationally tractable Bayesian inference. A ϕ-divergence-based influence analysis identifies the DFOS measurements that most strongly shape the posterior distributions, providing interpretable diagnostics of sensor informativeness and model adequacy. The calibrated parameters and embedded uncertainties are then transferred to a FEM of a full-scale structural configuration, enabling prediction of tendon breakage localization under realistic conditions. A separability analysis of the predictive strain distributions quantifies the identifiability of tendon breakage at varying depths, assessing the confidence with which different damage scenarios can be distinguished given the propagated uncertainties. Results demonstrate that the framework achieves robust parameter calibration, interpretable diagnostics, and uncertainty-informed damage detection, integrating experimental data, embedded MFU, and probabilistic modeling. By systematically propagating both experimental and model uncertainties, the approach supports reliable tendon breakage localization and optimal DFOS placement. Y1 - 2026 ER - TY - JOUR A1 - Kang, Chongjie A1 - Andrés Arcones, Daniel A1 - Becks, Henrik A1 - Beetz, Jakob A1 - Blankenbach, Jörg A1 - Claßen, Martin A1 - Degener, Sebastian A1 - Eisermann, Cedric A1 - Göbels, Anne A1 - Hegger, Josef A1 - Herrmann, Ralf A1 - Kähler, Philipp A1 - Peralta, Patricia A1 - Petryna, Yuri A1 - Schnellenbach-Held, Martina A1 - Schulz, Oliver A1 - Smarsly, Kay A1 - Fatih Sönmez, Mehmet A1 - Sprenger, Bjarne A1 - Unger, Jörg F. A1 - Vassilev, Hristo A1 - Weiser, Martin A1 - Marx, Steffen T1 - Intelligente digitale Methoden zur Verlängerung der Nutzungsdauer der Nibelungenbrücke JF - Beton- und Stahlbetonbau N2 - Um die Lebensdauer von Bauwerken unter Wahrung derer Standsicherheit und Funktionsfähigkeit zu verlängern, bedarf es effektiver Monitorings- sowie Instandhaltungskonzepte. Im Rahmen des von der Deutschen Forschungsgemeinschaft (DFG) geförderten Schwerpunktprogramms 2388 „Hundert plus – Verlängerung der Lebensdauer komplexer Baustrukturen durch intelligente Digitalisierung“ (kurz: SPP 100+) werden hierfür innovative, interdisziplinäre Methoden entwickelt und an der Nibelungenbrücke in Worms (NBW) validiert. Der vorliegende Beitrag stellt einige dieser neuentwickelten digitalen Methoden vor. Unter anderem umfasst dies zwei Systeme des Structural Health Monitoring (SHM) und deren zielorientierte Verknüpfung von mehreren Beschleunigungsmessdaten zur umfassenden Zustandsbewertung. Ergänzend werden innovative datenbasierte Simulationsmethoden zur Bestimmung des Temperaturfelds des Brückenüberbaus vorgestellt sowie mehrere Finite-Elemente-Modelle unterschiedlicher Detailtiefe präsentiert und miteinander verglichen. Abschließend werden innovative Methoden zum Verwalten des Bestandswissens von Brückenbauwerken diskutiert. Die Methoden wurden überwiegend unabhängig voneinander entwickelt und an der NBW validiert. Im nächsten Schritt werden die Methoden integriert, um die Instandhaltung der NBW zu unterstützen. Y1 - 2026 U6 - https://doi.org/10.1002/best.70070 VL - 121 IS - 4 SP - 303 EP - 320 ER - TY - GEN A1 - Kuroiwa, Ryo A1 - Shinano, Yuji A1 - Beck, J. Christopher T1 - Massively Parallel and Distributed Solvers for Domain-Independent Dynamic Programming N2 - In this paper, we develop distributed and parallel general-purpose solvers for combinatorial optimization through the framework of domain-independent dynamic programming (DIDP), a model-based paradigm based on dynamic programming. In particular, we parallelize heuristic state space search algorithms to develop such solvers. Benefiting from the general-purpose nature of DIDP, we apply our solvers to four problem classes: the traveling salesperson problem with time windows (TSPTW), the type1 simple assembly line balancing problem (SALBP-1), the one-to-one multi-commodity pickup and delivery traveling salesperson problem (m-PDTSP), and the type2 assembly line balancing problem with sequence-dependent setup times (SUALBP-2). We demonstrate the scalability of our solvers using up to 192 TB of RAM and 49,152 CPU cores. Using the developed solvers, we close 14 open instances of TSPTW, 49 of m-PDTSP, and 152 of SUALBP-2. T3 - ZIB-Report - 26-03 KW - Dynamic Programming, Combinatorial Optimization, Massively Parallel and Distributed Algorithms Y1 - 2026 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-103202 SN - 1438-0064 ER - TY - CHAP A1 - Kempke, Nils-Christian A1 - Koch, Thorsten T1 - A GPU accelerated variant of Schroeppel-Shamir’s algorithm for solving the market split problem T2 - Operations Research Proceedings 2025 N2 - The market split problem (MSP), introduced by Cornu´ejols and Dawande (1998), is a challenging binary optimization problem on which state-of-the-art linear programming-based branch-and-cut solvers perform poorly. We present a novel algorithm for solving the feasibility version of this problem, derived from Schroeppel–Shamir’s algorithm for the one-dimensional subset sum problem. Our approach is based on exhaustively enumerating one-dimensional solutions of MSP and utilizing GPUs to evaluate candidate solutions across the entire problem. The resulting hybrid CPU-GPU implementation significantly outperforms a parallel CPU-only variant, efficiently solving instances with up to 10 constraints and 90 variables. We demonstrate the algorithm’s performance on benchmark problems, solving instances of size (9, 80) in less than fifteen minutes and (10, 90) in up to one day. Given our results, sorting based algorithms can be considered competitive for solving the MSP on modern hardware. Y1 - 2026 ER - TY - JOUR A1 - Kempke, Nils-Christian A1 - Koch, Thorsten T1 - Fix-and-Propagate Heuristics Using Low-Precision First-Order LP Solutions for Large-Scale Mixed-Integer Linear Optimization JF - Mathematical Programming Computation N2 - We investigate the use of low-precision first-order methods (FOMs) within a fix-and-propagate (FP) framework for solving mixed-integer programming problems (MIPs). We employ GPU-accelerated PDLP, a variant of the Primal-Dual Hybrid Gradient (PDHG) method specialized to LP problems, to solve the LP-relaxation of our MIPs to low accuracy. This solution is used to motivate fixings within our FP framework. We evaluate the performance of our heuristic on MIPLIB 2017, demonstrating that low-accuracy LP solutions do not lead to a loss in the quality of the FP heuristic solutions. Further, we use our FP framework to produce high-accuracy solutions for large-scale (up to 243 million nonzeros and 8 million decision variables) unit commitment-based dispatch and expansion planning problems created with the modeling framework REMix. For the largest problems, we can generate solutions with a primal-dual gap of under 2% in less than 4 hours, whereas state-of-the-art commercial solvers cannot produce feasible solutions within 2 days of runtime. KW - Integer programming KW - Large scale optimization KW - Linear Programming KW - OR in energy KW - Primal heuristics Y1 - 2026 U6 - https://doi.org/10.1007/s12532-026-00312-7 ER - TY - JOUR A1 - Sikorski, Alexander A1 - Donati, Luca A1 - Weber, Marcus A1 - Schütte, Christof T1 - Effective Dynamics and Transition Pathways from Koopman-Inspired Neural Learning of Collective Variables N2 - The ISOKANN (Invariant Subspaces of Koopman Operators Learned by Artificial Neural Networks) framework provides a data-driven route to extract collective variables (CVs) and effective dynamics from complex molecular systems. In this work, we integrate the theoretical foundation of Koopman operators with Krylov-like subspace algorithms, and reduced dynamical modeling to build a coherent picture of how to describe metastable transitions in high-dimensional systems based on CVs. Starting from the identification of CVs based on dominant invariant subspaces, we derive the corresponding effective dynamics on the latent space and connect these to transition rates and times, committor functions, and transition pathways. The combination of Koopman-based learning and reduced-dimensional effective dynamics yields a principled framework for computing transition rates and pathways from simulation data. Numerical experiments on one-, two-, and three-dimensional benchmark potentials illustrate the ability of ISOKANN to reconstruct the coarse-grained kinetics and reproduce transition times across enthalpic and entropic barriers. Y1 - 2026 ER - TY - GEN A1 - Kharma, Sami A1 - Wies, Tobias A1 - Schintke, Florian T1 - Comprehensive Plugin-Based Monitoring of Nexflow Workflow Executions T2 - SCA/HPC Asia 2026 Y1 - 2026 UR - https://www.sc-asia.org/2026/data/poster/post140.pdf ER - TY - JOUR A1 - Schütte, Christof A1 - Sikorski, Alexander A1 - Kresse, Jakob A1 - Weber, Marcus T1 - On-the-Fly Lifting of Coarse Reaction-Coordinate Paths to Full-Dimensional Transition Path Ensembles N2 - Effective dynamics on a low-dimensional collective-variable (CV) or latent space can be simulated far more cheaply than the underlying high-dimensional stochastic system, but exploiting such coarse predictions requires lifting: turning a coarse CV trajectory into dynamically consistent full-dimensional states and path ensembles, without relying on global sampling of invariant or conditional fiber measures. We present a local, on-the-fly lifting strategy based on guided full-system trajectories. First an effective model in CV space is used to obtain a coarse reference trajectory. Then, an ensemble of full-dimensional trajectories is generated from a guided version of the original dynamics, where the guidance steers the trajectory to track the CV reference path. Because guidance biases the path distribution, we correct it via pathwise Girsanov reweighting, yielding a correct-by-construction importance-sampling approximation of the conditional law of the uncontrolled dynamics. We further connect the approach to stochastic optimal control, clarifying how coarse models can inform variance-reducing guidance for rare-event quantities. Numerical experiments demonstrate that inexpensive coarse transition paths can be converted into realistic full-system transition pathways (including barrier crossings and detours) and can accelerate estimation of transition pathways and statistics while providing minimal bias through weighted ensembles. Y1 - 2026 ER - TY - CHAP A1 - Ramaki, Niaz Mohammad A1 - Schintke, Florian T1 - Ensuring Reproducibility in Stream Processing with Blockchain Technologies T2 - 2025 11th International Conference on Computer and Communications (ICCC) Y1 - 2025 U6 - https://doi.org/10.1109/ICCC68654.2025.11437772 SP - 1383 EP - 1391 PB - IEEE ER - TY - JOUR A1 - Villani, Paolo A1 - Andrés Arcones, Daniel A1 - Unger, Jörg F. A1 - Weiser, Martin T1 - Gaussian mixture models for model improvement N2 - Modeling complex physical systems such as they arise in civil engineering applications requires finding a trade-off between physical fidelity and practicality. Consequently, deviations of simulation from measurements are ubiquitous even after model calibration due to the model discrepancy, which may result from deliberate modeling decisions, ignorance, or lack of knowledge. If the mismatch between simulation and measurements are deemed unacceptable, the model has to be improved. Targeted model improvement is challenging due to a non-local impact of model discrepancies on measurements and the dependence on sensor configurations. Many approaches to model improvement, such as Bayesian calibration with additive mismatch terms, gray-box models, symbolic regression, or stochastic model updating, often lack interpretability, generalizability, physical consistency, or practical applicability. This paper introduces a non-intrusive approach to model discrepancy analysis using mixture models. Instead of directly modifying the model structure, the method maps sensor readings to clusters of physically meaningful parameters, automatically assigning sensor readings to parameter vector clusters. This mapping can reveal systematic discrepancies and model biases, guiding targeted, physics-based refinements by the modeler. The approach is formulated within a Bayesian framework, enabling the identification of parameter clusters and their assignments via the Expectation-Maximization (EM) algorithm. The methodology is demonstrated through numerical experiments, including an illustrative example and a real-world case study of heat transfer in a concrete bridge. Y1 - 2026 ER - TY - CHAP A1 - Riccardi, Gabor A1 - Lindner, Niels T1 - On The Minimum-Weight Forward (Weakly) Fundamental Cycle Basis Problem in Directed Graphs T2 - International Network Optimization Conference 2026 (INOC 2026) N2 - The cycle space of a directed graph is generated by a cycle basis, where, in general, cycles are allowed to have both forward and backward arcs. In a forward cycle, all arcs have to follow the given direction. We study the existence, structure, and computational complexity of minimum-weight forward cycle bases in directed graphs. We give a complete structural characterization of digraphs that admit weakly fundamental (and hence integral) forward cycle bases, showing that this holds if and only if every block is either strongly connected or a single arc. We further provide an easily verifiable characterization of when a strongly connected digraph admits a forward fundamental cycle basis, proving that such a basis exists if and only if the set of directed cycles has cardinality equal to the cycle rank; in this case, the basis is unique and computable in polynomial time, and nonexistence can likewise be certified efficiently. Lastly, we show that while minimum-weight forward fundamental cycle bases can be found in polynomial time whenever they exist, the minimum-weight forward weakly fundamental cycle basis problem is NP-hard via a polynomial-time reduction from the minimum-weight weakly fundamental cycle basis problem on digraphs with metric weights. Y1 - 2026 ER - TY - CHAP A1 - Schelten, Niklas A1 - Christgau, Steffen A1 - Hutzler, Merit A1 - Kreowsky, Philipp A1 - De Lucia, Marco A1 - Schnor, Bettina A1 - Signer, Hannes A1 - Spazier, Johannes A1 - Stabernack, Benno A1 - Yahdzhyiev, Serhii T1 - Using FPGA-based Network-Attached Accelerators for Energy-Efficient AI Training in HPC Datacenters T2 - 2026 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) N2 - FPGA-based Network-Attached Accelerators offer a disaggregated alternative to closely-coupled FPGAs or GPUs, but their adoption is very limited due to missing hardware/software frameworks. However, the usability and energy-efficiency of FPGAs for HPC use-cases has been demonstrated previously. Yet, the support by HPC infrastructure for those devices is lacking behind other accelerators. This paper addresses this shortcoming and demonstrates a full-stack approach that seamlessly integrates Network-Attached Accelerators in HPC datacenters and allows flexible and scalable usage of FPGAs. The presented work includes the according framework, integration steps and a show-case application from the geoscience domain. We evaluate our approach by comparing the training of a Physics-Informed Neural Network on the NAA against two GPU implementations. The NAA reduces total energy consumption by about 50% and 17% compared to the Keras and CUDA implementation, respectively. These results demonstrate that FPGA-based NAAs can be successfully integrated into HPC environments and are a viable path toward more energy-efficient AI training. Y1 - 2026 ER - TY - CHAP A1 - Borndörfer, Ralf A1 - Jocas, Arturas A1 - Weiser, Martin T1 - An Eikonal Approach for Globally Optimal Free Flight Trajectories T2 - Computational Methods in Applied Sciences: Technologies for smart and sustainable transport systems N2 - We present an eikonal-based approach that is capable of finding a continuous globally optimal trajectory for an aircraft in a stationary wind field. This minimizes emissions and fuel consumption. If the destination is close to a cut locus of the associated Hamilton-Jacobi-Bellman equation, small numerical discretization errors can lead to selecting a merely locally optimal trajectory and missing the globally optimal one. Based on finite element error estimates, we construct a trust region around the cut loci in order to guarantee uniqueness of trajectories for destinations sufficiently far from cut loci. Y1 - 2026 ER - TY - JOUR A1 - Wuttke, Ulrike A1 - Stockmann, Ralf A1 - Winkler, Alexander T1 - Podcasts als bibliothekarisches Handlungsfeld JF - B.I.T. online (BIT online) : Bibliothek, Information, Technologie Y1 - 2026 SN - 1616-2617 VL - 29 IS - 1 SP - 48 EP - 53 ER - TY - CHAP A1 - Sunkara, Vikram A1 - Rostami, Atefe A1 - von Tycowicz, Christoph A1 - Schütte, Christof T1 - Stop throwing away your Decoder; extract the learnt local coordinate system using Latent-XAI T2 - The 4th World Conference on Explainable Artificial Intelligence (XAI-2026) Y1 - 2026 ER - TY - CHAP A1 - Schelten, Niklas A1 - Christgau, Steffen A1 - Schulte, Anton A1 - Schnor, Bettina A1 - Signer, Hannes A1 - Stabernack, Benno T1 - A Flexible Open-Source Framework for FPGA-based Network-Attached Accelerators using SpinalHDL T2 - Architecture of Computing Systems - 39th International Conference, ARCS 2026, Mainz, Germany, March 24-26, 2026, Proceedings. N2 - Domain-specific accelerators are increasingly vital in heterogeneous computing systems, driven by the demand for higher computational capacity and especially energy efficiency. Network-attached FPGAs promise a scalable and flexible alternative to closely coupled FPGAs for integrating accelerators into computing environments. While the advantages of specialized hardware implementations are apparent, traditional hardware development and integration remain time-consuming and complex. We present an open-source framework which combines a hardware shell with supporting software libraries, which enables fast development and deployment of FPGA-based network-attached accelerators. In contrast to traditional approaches using VHDL or Verilog, we leverage generative programming with SpinalHDL, providing a flexible hardware description with multi-level abstractions. This work eases the integration of accelerators into existing network infrastructures and simplifies adaptation to different FPGAs, eliminating complex and lengthy top-level hardware descriptions. Y1 - 2026 ER - TY - CHAP A1 - Franke, Paula A1 - Hamacher, Kay A1 - Manns, Paul T1 - Minimizing and Maximizing the Shannon Entropy for Fixed Marginals T2 - Operations Research Proceedings 2025 N2 - The mutual information (MI) between two random variables is an important correlation measure in data analysis. The Shannon entropy of a joint probability distri- bution is the variable part under fixed marginals. We aim to minimize and maximize it to obtain the largest and smallest MI possible in this case, leading to a scaled MI ratio for better comparability. We present algorithmic approaches and optimal solutions for a set of problem instances based on data from molecular evolution. We show that this allows us to construct a sensible, systematic correction to raw MI values. Y1 - 2026 ER - TY - CHAP A1 - Sengupta, Agniva A1 - Kuş, Dilara A1 - Li, Jianning A1 - Zachow, Stefan T1 - Globally Optimal Pose from Orthographic Silhouettes N2 - We solve the problem of determining the pose of known shapes in R^3 from their unoccluded silhouettes. The pose is determined up to global optimality using a simple yet under-explored property of the area-of-silhouette: its continuity w.r.t trajectories in the rotation space. The proposed method utilises pre-computed silhouette-signatures, modelled as a response surface of the area-of-silhouettes. Querying this silhouette-signature response surface for pose estimation leads to a strong branching of the rotation search space, making resolution-guided candidate search feasible. Additionally, we utilise the aspect ratio of 2D ellipses fitted to projected silhouettes as an auxiliary global shape signature to accelerate the pose search. This combined strategy forms the first method to efficiently estimate globally optimal pose from just the silhouettes, without being guided by correspondences, for any shape, irrespective of its convexity and genus. We validate our method on synthetic and real examples, demonstrating significantly improved accuracy against comparable approaches. Y1 - 2026 ER - TY - GEN A1 - Kempke, Nils-Christian A1 - Maher, Stephen John A1 - Rehfeldt, Daniel A1 - Gleixner, Ambros A1 - Koch, Thorsten A1 - Uslu, Svenja T1 - Distributed Parallel Structure-Aware Presolving for Arrowhead Linear Programs N2 - We present a structure-aware parallel presolve framework specialized to arrowhead linear programs (AHLPs) and designed for high-performance computing (HPC) environments, integrated into the parallel interior point solver PIPS-IPM++. Large-scale LPs arising from automated model generation frequently contain redundancies and numerical pathologies that necessitate effective presolve, yet existing presolve techniques are primarily serial or structure-agnostic and can become time-consuming in parallel solution workflows. Within PIPS-IPM++, AHLPs are stored in distributed memory, and our presolve builds on this to apply a highly parallel, distributed presolve across compute nodes while keeping communication overhead low and preserving the underlying arrowhead structure. We demonstrate the scalability and effectiveness of our approach on a diverse set of AHLPs and compare it against state-of-the-art presolve implementations, including PaPILO and the presolve implemented within Gurobi. Even on a single machine, our presolve significantly outperforms PaPILO by a factor of 18 and Gurobi’s presolve by a factor of 6 in terms of shifted geometric mean runtime, while reducing the problems by a similar amount to PaPILO. Using a distributed compute environment, we outperform Gurobi's presolve by a factor of 13. T3 - ZIB-Report - 26-01 KW - Linear Programming KW - Presolving KW - Large-Scale Optimization KW - Distributed Parallel Computing KW - Arrowhead Y1 - 2026 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-103034 SN - 1438-0064 ER - TY - CHAP A1 - Dinger, Patrick A1 - Gerber, Anja A1 - Gnyp, Anna A1 - von Hagel, Frank A1 - Hohmann, Georg A1 - Krause, Celia A1 - Wagner, Sarah A1 - Winkler, Alexander T1 - Sammlungsdaten als Forschungsdaten in den Digital Humanities T2 - Book of Abstracts - DHd 2026 Y1 - 2026 U6 - https://doi.org/10.5281/zenodo.18702753 SP - 69 EP - 74 ER - TY - JOUR A1 - Mehrmann, Carolin A1 - Johny, Jacob A1 - Ebbinghaus, Petra A1 - Hammerschmidt, Martin A1 - Das, Ankita A1 - Wei, Xin A1 - Tesch, Marc F. A1 - Rabe, Martin T1 - Nano IR spectroscopy on Silicon-Supported Organic–Inorganic Hybrid Materials JF - Phys. Chem. Chem. Phys. Y1 - 2026 U6 - https://doi.org/10.1039/D5CP03815D VL - 28 SP - 7352 PB - Royal Society of Chemistry (RSC) ER - TY - CHAP A1 - Pedersen, Jaap A1 - Lindner, Niels A1 - Rehfeldt, Daniel A1 - Koch, Thorsten T1 - Comparing Branching Rules for the Quota Steiner Tree Problem with Interference T2 - Operations Research Proceedings 2025 N2 - Branching decisions play a crucial role in branch-and-bound algorithms for solving combinatorial optimization problems. In this paper, we investigate several branching rules applied to the Quota Steiner Tree Problem with Interference (QSTPI). The Quota Steiner Tree Problem (QSTP) generalizes the classical Steiner Tree Problem (STP) in graphs by seeking a minimum-cost tree that connects a subset of profit-associated vertices whose cumulative profit meets or exceeds a given quota. The QSTPI introduces interference among vertices: Selecting certain vertices simultaneously reduces their individual contributions to the overall profit. This problem arises, for example, in positioning and connecting wind turbines, where turbines possibly shadow other turbines, reducing their energy yield. Unlike classical STP variants, large QSTPI instances require significantly more branching to compute provably optimal solutions. In contrast to branching on variables, we utilize the combinatorial structure of the QSTPI by branching on the graph's vertices. We adapt classical and problem-specific branching rules and present a comprehensive computational study comparing the effectiveness of these branching strategies. Y1 - 2026 ER - TY - JOUR A1 - Farias-Basulto, Guillermo A1 - Mehlhop, Thede A1 - Otto, Nicolas J. A1 - Bertram, Tobias A1 - Jäger, Klaus A1 - Gall, Stefan A1 - Weinberger, Nikolaus A1 - Schlatmann, Rutger A1 - Lauermann, Iver A1 - Klenk, Reiner A1 - List-Kratochvil, Emil A1 - Kaufmann, Christian A. T1 - Improving Perovskite/CIGS Tandem Solar Cells for Higher Power Conversion Efficiency through Light Management and Bandgap Engineering JF - ACS Appl. Mater. Interfaces Y1 - 2025 U6 - https://doi.org/10.1021/acsami.5c15458 VL - 17 SP - 56250 ER - TY - JOUR A1 - Brence, Blaž A1 - Brummer, Josephine A1 - Dercksen, Vincent J. A1 - Özel, Mehmet Neset A1 - Kulkarni, Abhishek A1 - Wolterhoff, Neele A1 - Prohaska, Steffen A1 - Hiesinger, Peter Robin A1 - Baum, Daniel T1 - Semi-automatic geometrical reconstruction and analysis of filopodia dynamics in 4D two-photon microscopy images JF - BMC Bioinformatics N2 - Background: Filopodia are thin and dynamic membrane protrusions that play a crucial role in cell migration, axon guidance, and other processes where cells explore and interact with their surroundings. Historically, filopodial dynamics have been studied in great detail in 2D in cultured cells, and more recently in 3D culture as well as living brains. However, there is a lack of efficient tools to trace and track filopodia in 4D images of complex brain cells. Results: To address this issue, we have developed a semi-automatic workflow for tracing filopodia in 3D images and tracking the traced filopodia over time. The workflow was developed based on high-resolution data of photoreceptor axon terminals in the in vivo context of normal Drosophila brain development, but devised to be applicable to filopodia in any system, including at different temporal and spatial scales. In contrast to the pre-existing methods, our workflow relies solely on the original intensity images without the requirement for segmentation or complex preprocessing. The workflow was realized in C++ within the Amira software system and consists of two main parts, dataset pre-processing, and geometrical filopodia reconstruction, where each of the two parts comprises multiple steps. In this paper, we provide an extensive workflow description and demonstrate its versatility for two different axo-dendritic morphologies, R7 and Dm8 cells. Finally, we provide an analysis of the time requirements for user input and data processing. Conclusion: To facilitate simple application within Amira or other frameworks, we share the source code, which is available at https://github.com/zibamira/filopodia-tool. Y1 - 2026 U6 - https://doi.org/10.1186/s12859-026-06385-4 VL - 27 ER - TY - JOUR A1 - Fischbach, Jan David A1 - Betz, Fridtjof A1 - Rebholz, Lukas A1 - Garg, Puneet A1 - Frizyuk, Kristina A1 - Binkowski, Felix A1 - Burger, Sven A1 - Hammerschmidt, Martin A1 - Rockstuhl, Carsten T1 - Pole-Expansion of the T-Matrix Based on a Matrix-Valued AAA-Algorithm JF - ArXiV Y1 - 2026 U6 - https://doi.org/10.48550/arXiv.2602.18414 SP - arXiv:2602.18414 ER - TY - CHAP A1 - Villim, Ján A1 - Nguyen, Martin A1 - Bobik, Pavol A1 - Genci, Jan A1 - Gecášek, Daniel T1 - New Version of the COR Simulation Engine T2 - Proceedings of 39th International Cosmic Ray Conference — PoS(ICRC2025) N2 - The COR simulation engine is a tool devoted to evaluating cosmic ray trajectories in Earth's magnetosphere. It is part of the COR System, available at https://cor.crmodels.org, and it also functions as a standalone command line tool. The former tool was published in 2022. We report the new version of the tool status with improved performance, precision, new functions/features, and refactored code. Y1 - 2025 U6 - https://doi.org/https://doi.org/10.22323/1.501.1377 VL - 501 PB - Sissa Medialab CY - Trieste, Italy ER - TY - JOUR A1 - Krüger, Jan A1 - Dopslaff, Sven A1 - Manley, Phillip A1 - Bergmann, Detlef A1 - Köning, Rainer A1 - Dai, Gaoliang A1 - Hahm, Kai A1 - Schneider, Philipp-Immanuel A1 - Hammerschmidt, Martin A1 - Zschiedrich, Lin A1 - Bosse, Harald A1 - Bodermann, Bernd T1 - Photomask linewidth measurement comparison including an improved model-based through-focus measurement approach JF - Meas. Sci. Technol. Y1 - 2026 U6 - https://doi.org/10.1088/1361-6501/ae44bb VL - 37 SP - 095004 ER - TY - JOUR A1 - Franke, Fabian A1 - Seeliger, Frank A1 - Stompor, Tomasz A1 - Wawra, Steffen T1 - Künstliche Intelligenz in Bibliotheken BT - Erfahrungen aus einem gemeinsamen Ideenworkshop des Bibliotheksverbunds Bayern und des Kooperativen Bibliotheksverbunds Berlin-Brandenburg JF - o-bib. Das offene Bibliotheksjournal N2 - Der Bibliotheksverbund Bayern und der Kooperative Bibliotheksverbund Berlin-Brandenburg haben in einem gemeinsamen Ideenworkshop im Mai 2025 Potenziale, Herausforderungen, Projekteansätze und Handlungsbedarfe bei Anwendung Künstlicher Intelligenz in Bibliotheken analysiert und diskutiert. Für die Handlungsfelder Interne Geschäftsgänge, Bibliotheksdienste, Forschungsnahe Dienste, Informationskompetenz und Analyse von Nutzungsdaten wurden Bedarfe identifiziert, Projektideen entwickelt und Handlungsempfehlungen formuliert. KW - künstliche Intelligenz KW - Bibliotheken Y1 - 2026 U6 - https://doi.org/10.5282/o-bib/6231 SN - 2363-9814 VL - 13 IS - 1 SP - 1 EP - 6 ER - TY - JOUR A1 - Gonnermann-Müller, Jana A1 - Haase, Jennifer A1 - Leins, Nicolas A1 - Igel, Moritz A1 - Fackeldey, Konstantin A1 - Pokutta, Sebastian T1 - FACET: Multi-Agent AI Supporting Teachers in Scaling Differentiated Learning for Diverse Students JF - arXiv N2 - Classrooms are becoming increasingly heterogeneous, comprising learners with diverse performance and motivation levels, language proficiencies, and learning differences such as dyslexia and ADHD. While teachers recognize the need for differentiated instruction, growing workloads create substantial barriers, making differentiated instruction an ideal that is often unrealized in practice. Current AI educational tools, which promise differentiated materials, are predominantly student-facing and performance-centric, ignoring other aspects that shape learning outcomes. We introduce FACET, a teacher-facing multi-agent framework designed to address these gaps by supporting differentiation that accounts for motivation, performance, and learning differences. Developed with educational stakeholders from the outset, the framework coordinates four specialized agents, including learner simulation, diagnostic assessment, material generation, and evaluation within a teacher-in-the-loop design. School principals (N = 30) shaped system requirements through participatory workshops, while in-service K-12 teachers (N = 70) evaluated material quality. Mixed-methods evaluation demonstrates strong perceived value for inclusive differentiation. Practitioners emphasized both the urgent need arising from classroom heterogeneity and the importance of maintaining pedagogical autonomy as a prerequisite for adoption. We discuss implications for future school deployment and outline partnerships for longitudinal classroom implementation. Y1 - 2026 U6 - https://doi.org/https://arxiv.org/abs/2601.22788 ER - TY - JOUR A1 - Raya-Moreno, Martí A1 - Dasch, Noah Alexy A1 - Farahani, Nasrin A1 - Gonzalez Oliva, Ignacio A1 - Gulans, Andris A1 - Hossain, Manoar A1 - Kleine, Hannah A1 - Kuban, Martin A1 - Lubeck, Sven A1 - Maurer, Benedikt A1 - Pavone, Pasquale A1 - Peschel, Fabian A1 - Popova-Gorelova, Daria A1 - Qiao, Lu A1 - Richter, Elias A1 - Rigamonti, Santiago A1 - Rodrigues Pelá, Ronaldo A1 - Sinha, Kshitij A1 - Speckhard, Daniel T. A1 - Tillack, Sebastian A1 - Tumakov, Dmitry A1 - Hong, Seokhyun A1 - Uzulis, Janis A1 - Voiculescu, Mara A1 - Vona, Cecilia A1 - Yang, Mao A1 - Draxl, Claudia T1 - An exciting approach to theoretical spectroscopy JF - Advanced Science Y1 - 2026 U6 - https://doi.org/10.1002/advs.76167 SP - e76167 ER - TY - JOUR A1 - Qiao, Lu A1 - Rodrigues Pelá, Ronaldo A1 - Draxl, Claudia T1 - First-principles Approach to Ultrafast Pump-probe Spectroscopy in Semiconductors JF - npj Comput. Mater. Y1 - 2026 U6 - https://doi.org/10.1038/s41524-026-02128-4 VL - 12 SP - 179 ER - TY - GEN A1 - Baumann, Felix A1 - Duda, Georg A1 - Schiela, Anton A1 - Weiser, Martin ED - Hintermüller, Michael ED - Herzog, Roland ED - Kanzow, Christian ED - Ulbrich, Michael ED - Ulbrich, Stefan T1 - Identification of Stress in Heterogeneous Contact Models BT - Simulation and Hierarchical Optimization, Part II T2 - Non-Smooth and Complementarity-Based Distributed Parameter Systems N2 - We develop a heterogeneous model of the lower limb system to simulate muscle forces and stresses acting on the knee joint. The modelling of the bone dynamics leads to an index-3 DAE, which we discretize by higher order collocation methods. Furthermore, we present an elastomechanical contact knee joint model of the articular cartilage. For the solution of the contact problem we develop an efficient multigrid solver, based on an Augmented-Lagrangian relaxation of the contact constraints. We formulate the identification of joint forces and resulting stresses with respect to different knee joint models as an inverse problem based on medical gait data. Y1 - 2026 VL - 173 PB - Springer Nature ER - TY - JOUR A1 - Rong, Guoyang A1 - Chen, Ying A1 - Koch, Thorsten A1 - Honda, Keisuke T1 - Assessing data quality in citation analysis: A case study of web of science and Crossref JF - Journal of Informetrics Y1 - 2026 U6 - https://doi.org/10.1016/j.joi.2026.101775 SN - 1751-1577 VL - 20 IS - 1 PB - Elsevier BV ER - TY - THES A1 - Villani, Paolo T1 - Regression Techniques for Surrogate Modelling in Bayesian Inverse Problems N2 - For many real-world applications, a system of interest can be represented via a mathematical model which depends on a set of parameters. In order to identify the parameters, a set of observations is available and an Inverse Problem is formulated. Identifying the parameters from the observations is often a challenging task, especially when the model is expensive to evaluate. This is the case for Partial Differential Equations models, where numerical simulations which are both inexact and computationally expensive are required to obtain the model output. To ease the computational costs, surrogate models can be used to approximate the forward model. In this work, we present two different regression techniques, Gaussian Process Regression and Lipschitz Regression. After reformulating the Inverse Problem to account for the surrogate model, we develope an adaptive training strategy to train the surrogate model. The proposed training strategy aims at optimizing not only the training points’ positions but also their evaluation accuracies. Moreover, interleaved sampling of the posterior distribution of the unknown parameters is performed while the surrogate model is trained, providing a solution for the Inverse Problem. The quality of the surrogating techniques as well as the effectiveness of the adaptive training strategy are tested through different numerical experiments. Y1 - 2025 ER - TY - CHAP A1 - Koch, Thorsten A1 - Kempke, Nils-Christian A1 - Lindner, Niels A1 - Mehl, Lukas A1 - Wetzel, Manuel A1 - Zittel, Janina T1 - High-Performance Robust Energy System Planning with Storage: A Single-LP Approach T2 - Proceedings of URBSENSE 2026 - 1st International Workshop on URBan SENSEmaking and Intelligence for Safer Cities Y1 - 2026 ER - TY - JOUR A1 - Hadjidimitriou, Natalia Selini A1 - Koch, Thorsten A1 - Lippi, Marco A1 - Petkovic, Milena A1 - Mamei, Marco T1 - Spatial analysis of COVID-19 and the Russia–Ukraine war impacts on natural gas flows using statistical and machine learning models JF - World Wide Web Y1 - 2026 U6 - https://doi.org/https://doi.org/10.1007/s11280-025-01402-7 SN - 1386-145X VL - 29 IS - 2 PB - Springer Science and Business Media LLC ER - TY - JOUR A1 - Ngokingha Tchouto, Mireille A1 - Mehl, Julia A1 - Khomeijani Farahani, Saeed A1 - Baum, Daniel A1 - Duda, Georg T1 - Novel image registration approach for combining 2D Osterix and collagen bundles images with 3D µCT JF - Journal of Bone and Mineral Research Y1 - 2026 U6 - https://doi.org/10.1093/jbmrpl/ziag009 VL - 10 IS - 3 ER - TY - JOUR A1 - Zimper, Sebastian A1 - Djurdjevac, Ana A1 - Hartmann, Carsten A1 - Schütte, Christof A1 - Conrad, Natasa Djurdjevac T1 - Mean-field optimal control with stochastic leaders N2 - We consider interacting agent systems with a large number of stochastic agents (or particles) influenced by a fixed number of external stochastic lead agents. Such examples arise, for example in models of opinion dynamics, where a small number of leaders (influencers) can steer the behaviour of a large population of followers. In this context, we study a partial mean-field limit where the number of followers tends to infinity, while the number of leaders stays constant. The partial mean-field limit dynamics is then given by a McKean-Vlasov stochastic differential equation (SDE) for the followers, coupled to a controlled Itô-SDE governing the dynamics of the lead agents. For a given cost functional that the lead agents seek to minimise, we show that the unique optimal control of the finite agent system convergences to the optimal control of the limiting system. This establishes that the low-dimensional control of the partial (mean-field) system provides an effective approximation for controlling the high-dimensional finite agent system. In addition, we propose a stochastic gradient descent algorithm that can efficiently approximate the mean-field control. Our theoretical results are illustrated on opinion dynamics model with lead agents, where the control objective is to drive the followers to reach consensus in finite time. Y1 - 2025 ER - TY - JOUR A1 - Kresse, Jakob A1 - Sikorski, Alexander A1 - Chewle, Surahit A1 - Sunkara, Vikram A1 - Weber, Marcus T1 - Revealing the Atomistic Mechanism of Rare Events in Molecular Dynamics JF - Journal of Chemical Theory and Computation N2 - Interpretable reaction coordinates are essential for understanding rare conformational transitions in molecular dynamics. The Atomistic Mechanism Of Rare Events in Molecular Dynamics (AMORE-MD) framework enhances interpretability of deep-learned reaction coordinates by connecting them to atomistic mechanisms, without requiring any a priori knowledge of collective variables, pathways, or endpoints. Here, AMORE-MD employs the ISOKANN algorithm to learn a neural membership function χ representing the dominant slow process, from which transition pathways are reconstructed as minimum-energy paths aligned with the gradient of χ, and atomic contributions are quantified through gradient-based sensitivity analysis. Iterative enhanced sampling further enriches transition regions and improves coverage of rare events enabling recovery of known mechanisms and chemically interpretable structural rearrangements at atomic resolution for the Müller-Brown potential, alanine dipeptide, and the elastin-derived hexapeptide VGVAPG. Y1 - 2026 U6 - https://doi.org/10.1021/acs.jctc.5c01906 VL - 22 IS - 5 SP - 2380 EP - 2389 ER - TY - JOUR A1 - Hartmann, Carsten A1 - Jöster, Annika A1 - Schütte, Christof A1 - Sikorski, Alexander A1 - Weber, Marcus T1 - Importance sampling of unbounded random stopping times: computing committor functions and exit rates without reweighting N2 - Rare events in molecular dynamics are often related to noise-induced transitions between different macroscopic states (e.g., in protein folding). A common feature of these rare transitions is that they happen on timescales that are on average exponentially long compared to the characteristic timescale of the system, with waiting time distributions that have (sub)exponential tails and infinite support. As a result, sampling such rare events can lead to trajectories that can be become arbitrarily long, with not too low probability, which makes the reweighting of such trajectories a real challenge. Here, we discuss rare event simulation by importance sampling from a variational perspective, with a focus on applications in molecular dynamics, in particular the computation of committor functions. The idea is to design importance sampling schemes that (a) reduce the variance of a rare event estimator while controlling the average length of the trajectories and (b) that do not require the reweighting of possibly very long trajectories. In doing so, we study different stochastic control formulations for committor and mean first exit times, which we compare both from a theoretical and a computational point of view, including numerical studies of some benchmark examples. Y1 - 2026 ER - TY - JOUR A1 - Abou Hamdan, Loubnan A1 - Jana, Aloke A1 - Colom, Rémi A1 - Aboujoussef, Nour A1 - Carlson, Cooper A1 - Overvig, Adam A1 - Binkowski, Felix A1 - Burger, Sven A1 - Genevet, Patrice T1 - A Complex-Frequency Framework for Kerker Unidirectionality in Photonic Resonators Y1 - 2026 U6 - https://doi.org/10.21203/rs.3.rs-8444305/v1 ER - TY - JOUR A1 - Wehlitz, Nathalie A1 - Pavliotis, Grigorios A1 - Schütte, Christof A1 - Winkelmann, Stefanie T1 - Data-driven Reduction of Transfer Operators for Particle Clustering Dynamics N2 - We develop an operator-based framework to coarse-grain interacting particle systems that exhibit clustering dynamics. Starting from the particle-based transfer operator, we first construct a sequence of reduced representations: the operator is projected onto concentrations and then further reduced by representing the concentration dynamics on a geometric low-dimensional manifold and an adapted finite-state discretization. The resulting coarse-grained transfer operator is finally estimated from dynamical simulation data by inferring the transition probabilities between the Markov states. Applied to systems with multichromatic and Morse interaction potentials, the reduced model reproduces key features of the clustering process, including transitions between cluster configurations and the emergence of metastable states. Spectral analysis and transition-path analysis of the estimated operator reveal implied time scales and dominant transition pathways, providing an interpretable and efficient description of particle-clustering dynamics. Y1 - 2026 ER - TY - JOUR A1 - Akhyar, Fatima-Zahrae A1 - Zhang, Wei A1 - Stoltz, Gabriel A1 - Schütte, Christof T1 - Generative modeling of conditional probability distributions on the level-sets of collective variables N2 - Given a probability distribution $\mu$ in $\mathbb{R}^d$ represented by data, we study in this paper the generative modeling of its conditional probability distributions on the level-sets of a collective variable $\xi: \mathbb{R}^d \rightarrow \mathbb{R}^k$, where $1 \le k