@misc{BuchholzKhebouriVuetal.2026, author = {Buchholz, Annika and Khebouri, Imene and Vu, Thi Huong and Koch, Thorsten and Kunt, Tim and Peters-Kottig, Wolfgang and Stompor, Tomasz and Zittel, Janina}, title = {Detecting and classifying publications based on their abstracts with LLM embeddings and multi-label classifiers}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-103273}, year = {2026}, language = {en} } @phdthesis{Amiranashvili2025, author = {Amiranashvili, Tamaz}, title = {Universal and Expressive Statistical Shape Models for Anatomical Structures}, school = {Technische Universit{\"a}t M{\"u}nchen}, pages = {98}, year = {2025}, abstract = {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.}, language = {en} } @article{SchaibleOezdemirDebusetal.2026, author = {Schaible, Jonas and {\"O}zdemir, Asena Karolin and Debus, Charlotte and Burger, Sven and Streit, Achim and Becker, Christiane and J{\"a}ger, Klaus and G{\"o}tz, Markus}, title = {Inverse Design of Optical Multilayer Thin Films using Robust Masked Diffusion Models}, journal = {ArXiV}, arxiv = {http://arxiv.org/abs/2604.01106}, doi = {10.48550/arXiv.2604.01106}, pages = {arXiv:2604.01106}, year = {2026}, language = {en} } @misc{AndresArconesPaulWeiseretal.2006, author = {Andr{\´e}s Arcones, Daniel and Paul, Aeneas and Weiser, Martin and Sanio, David and Mark, Peter and Unger, J{\"o}rg F.}, title = {Bayesian Tendon Break Localization under Model Uncertainty Using Distributed Fiber Optic Sensors Authors/Creators}, doi = {10.5281/zenodo.18713386}, year = {2006}, abstract = {Implementation, data and results of the paper "Bayesian Tendon Break Localization under Model Uncertainty Using Distributed Fiber Optic Sensors"}, language = {en} } @article{AndresArconesPaulWeiseretal.2026, author = {Andr{\´e}s Arcones, Daniel and Paul, Aeneas and Weiser, Martin and Sanio, David and Mark, Peter and Unger, J{\"o}rg F.}, title = {Bayesian Tendon Breakage Localization under Model Uncertainty Using Distributed Fiber Optic Sensors}, arxiv = {http://arxiv.org/abs/2604.08162}, year = {2026}, abstract = {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.}, language = {en} } @article{KangAndresArconesBecksetal.2026, author = {Kang, Chongjie and Andr{\´e}s Arcones, Daniel and Becks, Henrik and Beetz, Jakob and Blankenbach, J{\"o}rg and Claßen, Martin and Degener, Sebastian and Eisermann, Cedric and G{\"o}bels, Anne and Hegger, Josef and Herrmann, Ralf and K{\"a}hler, Philipp and Peralta, Patricia and Petryna, Yuri and Schnellenbach-Held, Martina and Schulz, Oliver and Smarsly, Kay and Fatih S{\"o}nmez, Mehmet and Sprenger, Bjarne and Unger, J{\"o}rg F. and Vassilev, Hristo and Weiser, Martin and Marx, Steffen}, title = {Intelligente digitale Methoden zur Verl{\"a}ngerung der Nutzungsdauer der Nibelungenbr{\"u}cke}, volume = {121}, journal = {Beton- und Stahlbetonbau}, number = {4}, doi = {10.1002/best.70070}, pages = {303 -- 320}, year = {2026}, abstract = {Um die Lebensdauer von Bauwerken unter Wahrung derer Standsicherheit und Funktionsf{\"a}higkeit zu verl{\"a}ngern, bedarf es effektiver Monitorings- sowie Instandhaltungskonzepte. Im Rahmen des von der Deutschen Forschungsgemeinschaft (DFG) gef{\"o}rderten Schwerpunktprogramms 2388 „Hundert plus - Verl{\"a}ngerung der Lebensdauer komplexer Baustrukturen durch intelligente Digitalisierung" (kurz: SPP 100+) werden hierf{\"u}r innovative, interdisziplin{\"a}re Methoden entwickelt und an der Nibelungenbr{\"u}cke 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 Verkn{\"u}pfung von mehreren Beschleunigungsmessdaten zur umfassenden Zustandsbewertung. Erg{\"a}nzend werden innovative datenbasierte Simulationsmethoden zur Bestimmung des Temperaturfelds des Br{\"u}cken{\"u}berbaus vorgestellt sowie mehrere Finite-Elemente-Modelle unterschiedlicher Detailtiefe pr{\"a}sentiert und miteinander verglichen. Abschließend werden innovative Methoden zum Verwalten des Bestandswissens von Br{\"u}ckenbauwerken diskutiert. Die Methoden wurden {\"u}berwiegend unabh{\"a}ngig voneinander entwickelt und an der NBW validiert. Im n{\"a}chsten Schritt werden die Methoden integriert, um die Instandhaltung der NBW zu unterst{\"u}tzen.}, language = {de} } @misc{KuroiwaShinanoBeck2026, author = {Kuroiwa, Ryo and Shinano, Yuji and Beck, J. Christopher}, title = {Massively Parallel and Distributed Solvers for Domain-Independent Dynamic Programming}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-103202}, year = {2026}, abstract = {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.}, language = {en} } @inproceedings{KempkeKoch2026, author = {Kempke, Nils-Christian and Koch, Thorsten}, title = {A GPU accelerated variant of Schroeppel-Shamir's algorithm for solving the market split problem}, booktitle = {Operations Research Proceedings 2025}, arxiv = {http://arxiv.org/abs/2507.05045}, year = {2026}, abstract = {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.}, language = {en} } @article{KempkeKoch2026, author = {Kempke, Nils-Christian and Koch, Thorsten}, title = {Fix-and-Propagate Heuristics Using Low-Precision First-Order LP Solutions for Large-Scale Mixed-Integer Linear Optimization}, journal = {Mathematical Programming Computation}, arxiv = {http://arxiv.org/abs/2503.10344}, doi = {10.1007/s12532-026-00312-7}, year = {2026}, abstract = {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.}, language = {en} } @article{SikorskiDonatiWeberetal.2026, author = {Sikorski, Alexander and Donati, Luca and Weber, Marcus and Sch{\"u}tte, Christof}, title = {Effective Dynamics and Transition Pathways from Koopman-Inspired Neural Learning of Collective Variables}, arxiv = {http://arxiv.org/abs/2604.05778}, year = {2026}, abstract = {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.}, language = {en} }