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 U6 - https://doi.org/10.48550/arXiv.2603.23266 ER - TY - GEN 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 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 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 - GEN 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 T2 - 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/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 Y1 - 2026 ER - TY - JOUR A1 - Qiao, Lu A1 - Rodrigues Pelá, Ronaldo A1 - Draxl, Claudia T1 - First-principles approach to ultrafast pump-probe spectroscopy in solids Y1 - 2026 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