TY - JOUR A1 - Schwope, A.D. A1 - Marsh, T.R. A1 - Parsons, S.G. A1 - Vogel, J. A1 - Dhillon, V.S. T1 - Unveiling the white dwarf in the eclipsing polar HU Aquarii JF - Astronomy & Astrophysics Y1 - 2025 U6 - https://doi.org/10.1051/0004-6361/202557306 ER - TY - JOUR A1 - Hölter, Arne A1 - Lemke, Mathias A1 - Weinzierl, Stefan A1 - Stein, Lewin T1 - Non-Reflecting Characteristic Boundary Conditions for Adjoint Time-Domain Acoustic Simulations JF - Journal of Theoretical and Computational Acoustics N2 - Accurate acoustic simulations in the free field require non-reflective boundary conditions to suppress spurious reflections at the computational domain boundaries. Although several characteristic-based formulations for direct (forward) simulations have been proposed in recent decades, the adjoint formulations of such characteristic-based boundary conditions (CBCs) have received limited atten- tion in the literature and lack a comprehensive analysis. This paper presents the derivation and evaluation of adjoint CBCs complementing the existing direct CBCs. Both the forward and adjoint CBCs are applied to the (nonlinear) Euler equations and linear acoustic equations in time-domain simulations. In this manner, the CBCs are investigated and subsequently compared to assess their respective accuracy and consistency. The CBCs were implemented using both a single-point and a zonal approach, with the former optionally combined with a sponge layer. Both approaches yielded comparable results in direct and adjoint simulations, while the zonal CBC exhibited improved ac- curacy at lower frequencies. Across the evaluated frequency range of approximately 350 – 5600 Hz, spurious reflections were attenuated by up to −70dB in both forward and adjoint cases, demon- strating the effectiveness and consistency of the proposed boundary treatment. Y1 - 2025 ER - TY - GEN A1 - Lindner, Niels A1 - Mehl, Lukas A1 - Bartoszuk, Karolina A1 - Berendes, Sarah A1 - Zittel, Janina T1 - Demand Uncertainty in Energy Systems: Scenario Catalogs vs. Integrated Robust Optimization N2 - Designing efficient energy systems is indispensable for shaping a more sustainable society. This involves making infrastructure investment decisions that must be valid for a long-term time horizon. While energy system optimization models constitute a powerful technique to support planning decisions, they need to cope with inherent uncertainty. For example, predicting future demand on a scale of decades is not only an intricate challenge in itself, but small fluctuations in such a forecast might also largely impact the layout of a complex energy system. In this paper, we compare two methodologies of capturing demand uncertainty for linear-programming based energy system optimization models. On one hand, we generate and analyze catalogs of varying demand scenarios, where each individual scenario is considered independently, so that the optimization produces scenario-specific investment pathways. On the other hand, we make use of robust linear programming to meet the demand of all scenarios at once. Since including a multitude of scenarios increases the size and complexity of the optimization model, we will show how to use warm-starting approaches to accelerate the computation process, by exploiting the similar structure of the linear program across different demand inputs. This allows to integrate a meaningful number of demand scenarios with fully-fledged energy system models. We demonstrate the practical use of our methods in a case study of the Berlin-Brandenburg area in Germany, a region that contains both a metropolitan area and its rural surroundings. As a backbone, we use the open-source framework oemof to create a sector-coupled optimization model for planning an energy system with up to 100% reduction of greenhouse gas emissions. This model features a fine-grained temporal resolution of one hour for the full year 2050. We consider uncertainty in demand for electricity, hydrogen, natural gas, central, and decentral heat. Based on our computations, we analyze the trade-offs in terms of quality and computation time for scenario catalogs and the robust optimization approach. We further demonstrate that our procedure provides a valuable strategy for decision makers to gain insight on the robustness and sensitivity of solutions regarding demand variability. T3 - ZIB-Report - 25-19 Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-102404 SN - 1438-0064 ER - TY - JOUR A1 - Gorgulla, Christoph A1 - Garza, Alejandro J. A1 - Kapil, Venkat A1 - Fackeldey, Konstantin T1 - QUASAR: A Flexible QM-MM Method for Biomolecular Systems based on Restraining Spheres JF - Computer Physics Communications N2 - Quantum mechanical models of molecules theoretically offer unprecedented accuracy in predicting values associated with these systems, including the free energy of interaction between two molecules. However, high-accuracy quantum mechanical methods are computationally too expensive to be applied to larger systems, including most biomolecular systems such as proteins. To circumvent this challenge, the hybrid quantum mechanics/molecular mechanics (QM/MM) method was developed, allowing one to treat only the most important part of the system on the quantum mechanical level and the remaining part on the classical level. To date, QM/MM simulations for biomolecular systems have been carried out almost exclusively on the electronic structure level, neglecting nuclear quantum effects (NQEs). Yet NQEs can play a major role in biomolecular systems [1]. Here, we present i-QI, a QM/MM client for the path integral molecular dynamics (PIMD) software i-PI [2, 3, 4]. i-QI allows for carrying out QM/MM simulations simultaneously, allowing for the inclusion of electronic as well as nuclear quantum effects. i-QI implements a new QM/MM scheme based on constraining potentials called QUASAR, which allows handling diffusive systems, such as biomolecules solvated in water solvent. The QUASAR method is suitable in particular when the properties of interest are equilibrium properties, such as the free energy of binding. i-QI is freely available and open source, and we demonstrate it on a test system. KW - QM-MM Methods KW - Path Integral KW - biomolecular systems KW - CADD KW - Free Energy KW - Binding Y1 - 2025 U6 - https://doi.org/10.1016/j.cpc.2025.109949 SN - 0010-4655 VL - 320 ER - TY - GEN A1 - Weiser, Martin A1 - Hubig, Michael A1 - Shanmugam Subramaniam, Jayant T1 - Reconstructing Ambient Temperature Changes in Death Time Estimation with a Bayesian Double-Exponential Approach T2 - Zenodo N2 - Code and data for the reconstruction of ambient temperature drop in time of death estimation We provide Octave code and temperature measurement data for - empirircally estimating thermal sensor likelihood - estimating time and amplitude of a single sudden ambient temperature drop from temperature measurement data in two thermally different compartments. Y1 - 2025 U6 - https://doi.org/10.5281/zenodo.17702240 ER - TY - JOUR A1 - Kempke, Nils-Christian A1 - Rehfeldt, Daniel A1 - Koch, Thorsten T1 - A Massively Parallel Interior-Point-Method for Arrowhead Linear Programs JF - SIAM Journal on Scientific Computing Y1 - 2025 ER - TY - JOUR A1 - Navayazdani, Esfandiar T1 - Ridge Regression on Riemannian Manifolds for Time-Series Prediction JF - Information Geometry Y1 - 2026 ER - TY - JOUR A1 - Sengupta, Agniva A1 - Bartoli, Adrien T1 - Convex Solutions to SfT and NRSfM under Algebraic Deformation Models JF - IEEE Transactions on Pattern Analysis and Machine Intelligence N2 - We present nonlinear formulations to Shape-from-Template (SfT) and Non-Rigid Structure-from-Motion (NRSfM) faithfully exploiting the isometric, conformal and equiareal deformation models. Existing work uses relaxations such as inextensibility or requires knowing the optic flow field around the correspondences, an impractical assumption. In contrast, the proposed formulations only require point correspondences and resolve all ambiguities using the notions of maximal depth and maximal isometry heuristics. We propose solution methods using Semi-Definite Programming (SDP) for all formulations. We show that straightforward SDP models conflict with the usual maximal depth heuristic and propose an adapted opposite-depth parameterisation demonstrating a lesser relaxation gap. Experimental results on many real-world benchmark datasets demonstrate superior accuracy over existing methods. Y1 - 2025 U6 - https://doi.org/10.1109/TPAMI.2025.3635039 ER - TY - JOUR A1 - Clausecker, Robert A1 - Lemire, Daniel A1 - Schintke, Florian T1 - Faster Positional‐Population Counts for AVX2, AVX‐512, and ASIMD JF - Concurrency and Computation: Practice and Experience Y1 - 2025 U6 - https://doi.org/10.1002/cpe.70435 SN - 1532-0626 VL - 37 IS - 27-28 PB - Wiley ER - TY - JOUR A1 - Biernacka, Katarzyna A1 - Castro, Leyla Jael A1 - Fritzsch, Bernadette A1 - Grossmann, Yves Vincent A1 - Reidelbach, Marco A1 - Struck, Alexander A1 - Lanza, Giacomo A1 - Walter, David A1 - Ulusoy, Inga T1 - From Vision to Implementation: Current Practices and Challenges of Software Management Plans in Research JF - 2nd Conference on Research Data Infrastructure (CoRDI) Y1 - 2025 U6 - https://doi.org/10.5281/zenodo.16735302 ER - TY - JOUR A1 - Fischer, Kristina A1 - Gerber, Anja A1 - Koprucki, Thomas A1 - Noback, Andres A1 - Reidelbach, Marco A1 - Schrade, Torsten A1 - Thiery, Florian T1 - Windows on Data: Federating Research Data with FAIR Digital Objects and Linked Open Data JF - 2nd Conference on Research Data Infrastructure (CoRDI) Y1 - 2025 U6 - https://doi.org/10.5281/zenodo.16736222 ER - TY - JOUR A1 - Marco, Reidelbach A1 - Marcus, Weber T1 - From Documentation to Discovery - MaRDMO and the MaRDI Ecosystem JF - 2nd Conference on Research Data Infrastructure (CoRDI) Y1 - 2025 U6 - https://doi.org/10.5281/zenodo.16735889 ER - TY - JOUR A1 - Schembera, Björn A1 - Wübbeling, Frank A1 - Shehu, Aurela A1 - Biedinger, Christine A1 - Fiedler, Jochen A1 - Reidelbach, Marco A1 - Schmidt, Burkhard A1 - Ferrer, Eloi A1 - Koprucki, Thomas T1 - FAIR Representation of Mathematical Research Data: MathModDB and MathAlgoDB as Knowledge Graphs for Mathematical Models and Numerical Algorithms JF - 2nd Conference on Research Data Infrastructure (CoRDI) Y1 - 2025 U6 - https://doi.org/10.5281/zenodo.16735911 ER - TY - JOUR A1 - Marco, Reidelbach T1 - MaRDMO: Future Gateway to FAIR Mathematical Data JF - E-Science-Tage 2025: Research Data Management: Challenges in a Changing World N2 - Mathematical research data plays a crucial role across scientific disciplines, yet its documentation and dissemination remain challenging due to the lack of standardized research data management practices. The MaRDMO Plugin addresses these challenges by integrating mathematical models, algorithms, and interdisciplinary workflows into the established framework of the Research Data Management Organiser (RDMO). Built on FAIR principles, MaRDMO enables structured documentation and retrieval of mathematical research data through guided questionnaires. It connects to multiple knowledge graphs, including MathModDB, MathAlgoDB, and the MaRDI Portal. Users can document and search for models, algorithms, and workflows via dynamic selection interfaces that also leverage other sources such as Wikidata. The plugin facilitates the export to the individual MaRDI services, ensuring data quality through automated validation. By embedding mathematical research data management into the widely adopted RDMO platform, MaRDMO represents a significant step toward making mathematical research data more findable, accessible, and reusable. Y1 - 2025 U6 - https://doi.org/10.11588/heibooks.1652.c23913 SP - 40 EP - 53 ER - TY - CHAP A1 - Schembera, Björn A1 - Wübbeling, Frank A1 - Kleikamp, Hendrik A1 - Schmidt, Burkhard A1 - Shehu, Aurela A1 - Reidelbach, Marco A1 - Biedinger, Christine A1 - Fiedler, Jochen A1 - Koprucki, Thomas A1 - Iglezakis, Dorothea A1 - Göddeke, Dominik T1 - Towards a Knowledge Graph for Models and Algorithms in Applied Mathematics T2 - Metadata and Semantic Research. MTSR 2024 N2 - Mathematical models and algorithms are an essential part of mathematical research data, as they are epistemically grounding numerical data. To make this research data FAIR, we present how two previously distinct ontologies, MathAlgoDB for algorithms and MathModDB for models, were merged and extended into a living knowledge graph as the key outcome. This was achieved by connecting the ontologies through computational tasks that correspond to algorithmic tasks. Moreover, we show how models and algorithms can be enriched with subject-specific metadata, such as matrix symmetry or model linearity, essential for defining workflows and determining suitable algorithms. Additionally, we propose controlled vocabularies to be added, along with a new class that differentiates base quantities from specific use case quantities. We illustrate the capabilities of the developed knowledge graph using two detailed examples from different application areas of applied mathematics, having already integrated over 250 research assets into the knowledge graph. Y1 - 2025 SN - 9783031819735 U6 - https://doi.org/10.1007/978-3-031-81974-2_8 SN - 1865-0929 VL - 2331 SP - 95 EP - 109 PB - Springer Nature Switzerland CY - Cham ER - TY - JOUR A1 - Sekulic, Ivan A1 - Schaible, Jonas A1 - Müller, Gabriel A1 - Plock, Matthias A1 - Burger, Sven A1 - Martínez-Lahuerta, Víctor José A1 - Gaaloul, Naceur A1 - Schneider, Philipp-Immanuel T1 - Physics-informed Bayesian optimization of expensive-to-evaluate black-box functions JF - Mach. Learn. Sci. Technol. N2 - Abstract Bayesian optimization with Gaussian process surrogates is a popular approach for optimizing expensive-to-evaluate functions in terms of time, energy, or computational resources. Typically, a Gaussian process models a scalar objective derived from observed data. However, in many real-world applications, the objective is a combination of multiple outputs from physical experiments or simulations. Converting these multidimensional observations into a single scalar can lead to information loss, slowing convergence and yielding suboptimal results. To address this, we propose to use multi-output Gaussian processes to learn the full vector of observations directly, before mapping them to the scalar objective via an inexpensive analytical function. This physics-informed approach retains more information from the underlying physical processes, improving surrogate model accuracy. As a result, the approach accelerates optimization and produces better final designs compared to standard implementations. Y1 - 2025 U6 - https://doi.org/10.1088/2632-2153/ae1f5f VL - 6 SP - 040503 PB - IOP Publishing ER - TY - CHAP A1 - Schummel, Philip A1 - Teichmann, Malte A1 - Gonnermann-Müller, Jana T1 - Specifying ten roles of using chatgpt in secondary education: a teacher´s perspective T2 - Proceedings of the European Conference on Information Systems N2 - ChatGPT can benefit education; however, challenges arise that demand competent and responsible usage. Competence development needs to start in schools, with the teacher being the gatekeeper for integrating it into the teaching process. Existing research suggests that schoolteachers lack specific guidance on integrating ChatGPT into teaching, as current discussions remain high-level and omit specific use cases throughout the entire teaching process. To guide an informed usage, this paper presents the results of a systematic literature review, analyzing 38 selected papers on investigating ChatGPT in schools. This selection is based on rigorously defined criteria, adhering to the PRISMA guidelines, initially screening 1075 papers. The central value of this paper is to aggregate research into ten roles for secondary teachers on using ChatGPT in schools, presenting corresponding use cases throughout the teaching process. Future research validates the ten roles, conducting interviews with teachers to present an exhaustive framework for integrating ChatGPT in schoolteacher practice. Y1 - 2025 UR - https://aisel.aisnet.org/ecis2025/education/education/3/ ER - TY - CHAP A1 - Kera, Hiroshi A1 - Pelleriti, Nico A1 - Ishihara, Yuki A1 - Zimmer, Max A1 - Pokutta, Sebastian T1 - Computational Algebra with Attention: Transformer Oracles for Border Basis Algorithms T2 - Proceedings of the Conference on Neural Information Processing Systems Y1 - 2025 ER - TY - CHAP A1 - Gonnermann-Müller, Jana A1 - Sahling, Kristina A1 - Haase, Jennifer T1 - Let's Be Realistic: AI-Recommender Use in a Complex Management Setting T2 - CHI EA '25: Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems Y1 - 2025 U6 - https://doi.org/10.1145/3706599.3720131 ER - TY - JOUR A1 - Leins, Nicolas A1 - Gonnermann-Müller, Jana A1 - Teichmann, Malte T1 - Comparing head-mounted and handheld augmented reality for guided assembly JF - Journal on Multimodal User Interfaces Y1 - 2024 ER - TY - JOUR A1 - Gonnermann-Müller, Jana A1 - Krüger, Jule M. T1 - Unlocking Augmented Reality Learning Design Based on Evidence From Empirical Cognitive Load Studies—A Systematic Literature Review JF - Journal of Computer Assisted Learning Y1 - 2024 ER - TY - CHAP A1 - Głuch, Grzegorz A1 - Turan, Berkant A1 - Nagarajan, Sai Ganesh A1 - Pokutta, Sebastian T1 - The Good, the Bad and the Ugly: Meta-Analysis of Watermarks, Transferable Attacks and Adversarial Defenses T2 - Proceedings of the Conference on Neural Information Processing Systems Y1 - 2025 ER - TY - JOUR A1 - Hoen, Alexander A1 - Kamp, Dominik A1 - Gleixner, Ambros T1 - MIP-DD: Delta Debugging for Mixed-Integer Programming Solvers JF - INFORMS Journal on Computing Y1 - 2025 U6 - https://doi.org/10.1287/ijoc.2024.0844 ER - TY - CHAP A1 - Gonnermann-Müller, Jana A1 - Leins, Nicolas A1 - Gronau, Norbert A1 - Kosch, Thomas T1 - Value by design: Reducing Cognitive Load by Using Visual Guidance in Augmented RealityAn Eye-Tracking Study T2 - Proceedings of the International Conference on Information Systems Y1 - 2024 ER - TY - CHAP A1 - Gonnermann-Müller, Jana A1 - Brandenburger, Bonny A1 - Vladova, Gergana A1 - Gronau, Norbert T1 - To What Extent Can Individualisation in Terms of Different Types of Mode Improve Learning Outcomes and Learner Satisfaction? A Pre-study T2 - Proceedings of the Hawaii International Conference on System Sciences Y1 - 2023 ER - TY - JOUR A1 - Hendrych, Deborah A1 - Troppens, Hannah A1 - Besançon, Mathieu A1 - Pokutta, Sebastian T1 - Convex mixed-integer optimization with Frank-Wolfe methods JF - Mathematical Programming Computation Y1 - 2025 U6 - https://doi.org/10.1007/s12532-025-00288-w VL - 17 SP - 731 EP - 757 ER - TY - CHAP A1 - Kumano, Soichiro A1 - Kera, Hiroshi A1 - Yamasaki, Toshihiko T1 - Wide Two-Layer Networks can Learn from Adversarial Perturbations T2 - Proceedings of the Conference on Neural Information Processing Systems Y1 - 2024 ER - TY - CHAP A1 - Fukuda, Takuma A1 - Kera, Hiroshi A1 - Kawamoto, Kazuhiko T1 - Adapter Merging with Centroid Prototype Mapping for Scalable Class-Incremental Learning T2 - 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Y1 - 2025 U6 - https://doi.org/10.1109/CVPR52734.2025.00460 SP - 4884 EP - 4893 ER - TY - CHAP A1 - Kera, Hiroshi A1 - Ishihara, Yuki A1 - Kambe, Yuta A1 - Vaccon, Tristan A1 - Yokoyama, Kazuhiro T1 - Learning to Compute Gr\"obner Bases T2 - Proceedings of the Conference on Neural Information Processing Systems Y1 - 2024 ER - TY - GEN A1 - Ebert, Patricia A1 - Schlechte, Thomas A1 - Schwartz, Stephan T1 - Scheduling for German Road Inspectors N2 - For the yearly over 500,000 vehicle inspections of the German Federal Logistics and Mobility Office (BALM), crew rosters must be scheduled to efficiently achieve Germany's road inspection control targets. For that, we present a model to solve the respective duty scheduling and crew rostering problem in order to obtain duty rosters that comply with numerous legal regulations while maximizing the 'control success' to achieve the control targets. We formulate the Template Assignment Problem, which can be modelled as a large scale mixed-integer linear program. Here, feasible combinations of control topics are assigned to the duties using a hypergraph approach. The model is used in production by BALM, and we prove its effectiveness on a number of real-world instances. T3 - ZIB-Report - 25-13 KW - scheduling KW - rostering KW - mixed-integer linear program KW - crew scheduling KW - hypergraph Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-101999 SN - 1438-0064 ER - TY - GEN A1 - Betz, Fridtjof A1 - Binkowski, Felix A1 - Fischbach, Jan David A1 - Feldman, Nick A1 - Zschiedrich, Lin A1 - Rockstuhl, Carsten A1 - Koenderink, A. Femius A1 - Burger, Sven T1 - Uncovering Hidden Resonances in Non-Hermitian Systems with Scattering Thresholds (Laser Photonics Rev. 19(21)/2025) T2 - Laser Photonics Rev. Y1 - 2025 U6 - https://doi.org/10.1002/lpor.70534 VL - 19 IS - 21 SP - e70534 ER - TY - CHAP A1 - Manogue, Kevin A1 - Schang, Tomasz A1 - Kuş, Dilara A1 - Müller, Jonas A1 - Zachow, Stefan A1 - Sengupta, Agniva T1 - Generalizing Shape-from-Template to Topological Changes T2 - Smart Tools and Applications in Graphics - Eurographics Italian Chapter Conference N2 - Reconstructing the surfaces of deformable objects from correspondences between a 3D template and a 2D image is well studied under Shape-from-Template (SfT) methods; however, existing approaches break down when topological changes accompany the deformation. We propose a principled extension of SfT that enables reconstruction in the presence of such changes. Our approach is initialized with a classical SfT solution and iteratively adapts the template by partitioning its spatial domain so as to minimize an energy functional that jointly encodes physical plausibility and reprojection consistency. We demonstrate that the method robustly captures a wide range of practically relevant topological events including tears and cuts on bounded 2D surfaces, thereby establishing the first general framework for topological-change-aware SfT. Experiments on both synthetic and real data confirm that our approach consistently outperforms baseline methods. Y1 - 2025 SN - 978-3-03868-296-7 U6 - https://doi.org/10.2312/stag.20251322 PB - The Eurographics Association ER - TY - GEN A1 - Sekulic, Ivan A1 - Schaible, Jonas A1 - Müller, Gabriel A1 - Plock, Matthias A1 - Burger, Sven A1 - Martinez-Lahuerta, Victor J. A1 - Gaaloul, Naceur A1 - Schneider, Philipp-Immanuel T1 - Data publication for Physics-informed Bayesian optimization of expensive-to-evaluate black-box functions T2 - Zenodo Y1 - 2025 U6 - https://doi.org/10.5281/zenodo.16751507 ER - TY - THES A1 - Plock, Matthias T1 - On Methods for Bayesian Optimization of Least Squares Problems and Optimization of Nanophotonic Devices Y1 - 2025 U6 - https://doi.org/10.17169/refubium-48179 ER - TY - CHAP A1 - Zschiedrich, Lin A1 - Betz, Fridtjof A1 - Binkowski, Felix A1 - Kuen, Lilli A1 - Hammerschmidt, Martin A1 - Burger, Sven T1 - Efficient Photonic Component Analysis via AAA Rational Approximation T2 - 2025 International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD) Y1 - 2025 U6 - https://doi.org/10.1109/NUSOD64393.2025.11199710 SP - 115 EP - 116 ER - TY - GEN A1 - Zakiyeva, Nazgul A1 - Petkovic, Milena T1 - Large-scale functional network time series model solved with mathematical programming approach T2 - Econometrics and Statistics N2 - A functional network autoregressive model is proposed for studying large-scale network time series observed at high temporal resolution. The model incorporates high-dimensional curves to capture both serial and cross-sectional dependence in large-scale network functional time series. Estimation of the model is approached using a Mixed Integer Optimization method. Simulation studies confirm the consistency of parameter and adjacency matrix estimation. The method is applied to data from a real-life natural gas supply network. Compared to alternative prediction models, the proposed model delivers more accurate day-ahead hourly out-of-sample forecasts of the gas inflows and outflows at most gas nodes. T3 - ZIB-Report - 25-17 Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-101936 ER - TY - JOUR A1 - Liu, Zichen A1 - Zhang, Wei A1 - Li, Tiejun T1 - Improving the Euclidean Diffusion Generation of Manifold Data by Mitigating Score Function Singularity JF - NeurIPS 2025 N2 - Euclidean diffusion models have achieved remarkable success in generative modeling across diverse domains, and they have been extended to manifold case in recent advances. Instead of explicitly utilizing the structure of special manifolds as studied in previous works, we investigate direct sampling of the Euclidean diffusion models for general manifold-constrained data in this paper. We reveal the multiscale singularity of the score function in the embedded space of manifold, which hinders the accuracy of diffusion-generated samples. We then present an elaborate theoretical analysis of the singularity structure of the score function by separating it along the tangential and normal directions of the manifold. To mitigate the singularity and improve the sampling accuracy, we propose two novel methods: (1) Niso-DM, which introduces non-isotropic noise along the normal direction to reduce scale discrepancies, and (2) Tango-DM, which trains only the tangential component of the score function using a tangential-only loss function. Numerical experiments demonstrate that our methods achieve superior performance on distributions over various manifolds with complex geometries. Y1 - 2025 ER - TY - GEN A1 - Shinano, Yuji A1 - Vigerske, Stefan T1 - Smoothie: Mixing the strongest MIP solvers to solve hard MIP instances on supercomputers - Phase I development N2 - Mixed-Integer Linear Programming (MIP) is applicable to such a wide range of real-world decision problems that the competition for the best code to solve such problems has lead to tremendous progress over the last decades. While current solvers can solve some of the problems that seemed completely out-of-reach just 10 years ago, there are always relevant MIP problems that currently cannot be solved. With the Smoothie solver we intend to solve extremely hard MIP problems by building on the many years that went into the development of several state-of-the-art MIP solvers and by utilizing some of the largest computing resources available. The high-level task parallelization framework UG (Ubiquity Generator) is used and extended by Smoothie to build a solver that uses large-scale parallelization to distribute the solution of a single MIP on a shared- or distributed-memory computing infrastructure, thereby employing several established MIP solvers simultaneously. For the first development phase, which is the topic of this report, both FICO Xpress and Gurobi are used in concurrent mode on a single machine, while information on incumbent solutions and explored branch-and-bound subtrees is exchanged. A dynamic restarting mechanism ensures that solver configurations are selected that promise most suitable for the MIP to be solved. We report on initial findings using this early version of Smoothie on unsolved problems from MIPLIB 2017. T3 - ZIB-Report - 25-18 KW - MIPLIB, MIP, UG, Parallelization Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-101906 SN - 1438-0064 ER - TY - JOUR A1 - Zakiyeva, Nazgul A1 - Petkovic, Milena T1 - Large-scale functional network time series model solved with mathematical programming approach JF - Econometrics and Statistics Y1 - 2025 U6 - https://doi.org/10.1016/j.ecosta.2025.10.001 SN - 2452-3062 PB - Elsevier BV ER - TY - CHAP A1 - Bortoletto, Enrico A1 - Lindner, Niels ED - Voigt, Guido ED - Fliedner, Malte ED - Haase, Knut ED - Brüggemann, Wolfgang ED - Hoberg, Kai ED - Meissner, Jörn T1 - Scaling and Rounding Periodic Event Scheduling Instances to Different Period Times T2 - Operations Research Proceedings 2023 N2 - The Periodic Event Scheduling Problem (PESP) is a notoriously hard combinatorial optimization problem, essential for the design of periodic timetables in public transportation. The coefficients of the integer variables in the standard mixed integer linear programming formulations of PESP are the period time, e.g., 60 for a horizon of one hour with a resolution of one minute. In many application scenarios, lines with different frequencies have to be scheduled, leading to period times with many divisors. It then seems natural to consider derived instances, where the period time is a divisor of the original one, thereby smaller, and bounds are scaled and rounded accordingly. To this end, we identify two rounding schemes: wide and tight. We then discuss the approximation performance of both strategies, in theory and practice. Y1 - 2025 U6 - https://doi.org/10.1007/978-3-031-58405-3_51 SP - 397 EP - 402 ER - TY - CHAP A1 - Lindner, Niels A1 - Liebchen, Christian ED - Voigt, Guido ED - Fliedner, Malte ED - Haase, Knut ED - Brüggemann, Wolfgang ED - Hoberg, Kai ED - Meissner, Jörn T1 - Incremental Heuristics for Periodic Timetabling T2 - Operations Research Proceedings 2023 N2 - We present incremental heuristics for the Periodic Event Scheduling Problem (PESP), the standard mathematical tool to optimize periodic timetables in public transport. The core of our method is to solve successively larger subinstances making use of previously found solutions. Introducing the technical notion of free stratifications, we formulate a general scheme for incremental heuristics for PESP. More practically, we use line and station information to create heuristics that add lines or stations one by one, and we evaluate these heuristics on instances of the benchmarking library PESPlib. This approach is indeed viable, and leads to new incumbent solutions for six PESPlib instances. Y1 - 2025 U6 - https://doi.org/10.1007/978-3-031-58405-3_59 SP - 461 EP - 467 PB - Springer Nature Switzerland CY - Cham ER - TY - JOUR A1 - Krishnadas, Anirudh A1 - Moshi, Maryam A1 - Quintana, Ramon Alain Miranda A1 - Fournier, Rene T1 - Geometric Features and a Neural Network Classifier for Detecting Melting-Like Transitions in Clusters. JF - AIP Publications - The Journal of Chemical Physics N2 - Melting-like transitions in clusters are normally identified by a peak in the heat capacity curve C(T ) at T = Tc. Computing C(T ) requires costly simulations with millions of steps. We discuss four easily calculated functions of temperature that help detect and characterize melting-like transitions. The first, f1 (or WU ), is the width of the potential energy distribution which shows an abrupt increase near Tc. The other three are statistics of the ordered set of N(N − 1)/2 interatomic distances rij: (i) f2 is a measure of dissimilarity to the lowest energy configuration, or global minimum; (ii) f3 is the effective number of rij’s found in a small interval centered around (r1 + r2)/2 where r1, r2 are the positions of the first two peaks in the pair distribution function; and (iii) f4 is a measure of non-uniformity in the distribution of the ri j’s. Numerical tests with empirical potentials that model three types of bonding (van der Waals, covalent, and metallic) show that f1, f2, f3, and f4 produce estimates for the middle of the melting region in general agreement with Tc. An Artificial Neural Network (ANN) classifier that takes, as inputs, f2, f3, and many variants of f4, is used to calculate the solid fraction FS(T) and find the solid-liquid coexistence region between freezing and melting temperatures, [Tf , Tm]. Inflection points in f3(T ) and FS(T ) are very sensitive indicators of phase transitions. Estimates of Tc obtained from them converge one to three orders of magnitude faster, in simulation time, than those obtained with C(T ). Y1 - 2025 ER - TY - JOUR A1 - Krishnadas, Anirudh A1 - Charron, Nicolas A1 - Fournier, Rene T1 - Melting Transitions in Small Aluminum Clusters Simulated with Energies Approaching DFT Accuracy. JF - ACS Publications- Journal of Chemical Theory and Computation N2 - We describe a computational framework for modelling melting-like transitions in atomic clusters that combines first-principles energy calculations, global optimization, and machine-learned interatomic potentials. A diverse set of configurations is generated by global optimization, and energies are calculated by Density Functional Theory. The energies are fitted to an accuracy of 10 meV/atom or better with an Allegro E(3)-equivariant neural network potential. The model allows efficient parallel tempering Monte Carlo simulations with near DFT-level accuracy. This methodology was validated by simulating Na_20 and comparing it to earlier experimental and computational results. We used it to study melting-like transitions in Al_n+ clusters (n=9 to 16), and Al_n and Al_n^- (n=12, 13,14). The simulated heat capacity of these clusters, in particular Al_16+, are in qualitative agreement with experiments. The melting point of Al_n+ clusters with n=11-16 are well above the bulk melting point (934 K). The closed-shell Al_13- species has an exceptionally high melting point, close to 2100 K. Y1 - 2025 ER - TY - JOUR A1 - Krishnadas, Anirudh A1 - Sinha, Nidhi A1 - Kirchner, Tom A1 - Antony, Bobby T1 - Calculation of electron-impact ionization of various benzene derivatives JF - IOP Publications- Physica Scripta N2 - Calculations of the electron-impact ionization cross-section of pyrene, anthracene, benzoyl chloride, benzophenone, and phthalonitrile are reported over a wide energy range. A comparison of theoretical models, viz. spherical complex optical potential (SCOP), pixel counting method (PCM), and the binary encounter Bethe (BEB) model is carried out. SCOP calculations provide the inelastic cross-section, and the ionization cross-section is extracted from it. This result is modified with PCM, a model previously applied to ion collisions which takes geometric screening corrections into account. The BEB model is used as an independent approach to calculate the ionization cross-section. It is demonstrated that all model results are in reasonable agreement with each other. Comparison is also made with other theoretical data where available. Y1 - 2024 U6 - https://doi.org/10.1088/1402-4896/ad640a VL - 99 IS - 9 SP - 095403 ER - TY - CHAP A1 - Löbel, Fabian A1 - Lindner, Niels ED - Sauer, Jonas ED - Schmidt, Marie T1 - A Geometric Approach to Integrated Periodic Timetabling and Passenger Routing T2 - 25th Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2025) N2 - We offer a geometric perspective on the problem of integrated periodic timetabling and passenger routing in public transport. Inside the space of periodic tensions, we single out those regions, where the same set of paths provides shortest passenger routes. This results in a polyhedral subdivision, which we combine with the known decomposition by polytropes. On each maximal region of the common refinement, the integrated problem is solvable in polynomial time. We transform these insights into a new geometry-driven primal heuristic, integrated tropical neighborhood search (ITNS). Computationally, we compare implementations of ITNS and the integrated (restricted) modulo network simplex algorithm on the TimPassLib benchmark set, and contribute better solutions in terms of total travel time for all but one of the twenty-five instances for which a proven optimal solution is not yet known. KW - Periodic Timetabling KW - Passenger Routing KW - Polyhedral Complexes Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0030-drops-247580 SN - 978-3-95977-404-8 SN - 2190-6807 VL - 137 SP - 2:1 EP - 2:19 PB - Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik CY - Dagstuhl, Germany ER - TY - CHAP A1 - Ghannam, Mohammed A1 - Gleixner, Ambros T1 - Hybrid Genetic Search for Dynamic Vehicle Routing with Time Windows T2 - Operations Research Proceedings 2023. OR 2023 Y1 - 2025 U6 - https://doi.org/10.1007/978-3-031-58405-3_35 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 scales spindle elongation but not chromosome segregation in C. elegans JF - bioRxiv N2 - How embryos adapt their internal cellular machinery to reductions in cell size during development remains a fundamental question in cell biology. Here, we use high-resolution lattice light-sheet fluorescence microscopy and automated image analysis to quantify lineage-resolved mitotic spindle and chromosome segregation dynamics from the 2– to 64–cell stages in Caenorhabditis elegans embryos. While spindle length scales with cell size across both wild-type and size-perturbed embryos, chromosome segregation dynamics remain largely invariant, suggesting that distinct mechanisms govern these mitotic processes. Combining femtosecond laser ablation with large-scale electron tomography, we find that central spindle microtubules mediate chromosome segregation dynamics and remain uncoupled from cell size across all stages of early development. In contrast, spindle elongation is driven by cortically anchored motor proteins and astral microtubules, rendering it sensitive to cell size. Incorporating these experimental results into an extended stoichiometric model for both the spindle and chromosomes, we find that allowing only cell size and microtubule catastrophe rates to vary reproduces elongation dynamics across development. The same model also accounts for centrosome separation and pronuclear positioning in the one-cell C. elegans embryo, spindle-length scaling across nematode species spanning ~100 million years of divergence, and spindle rotation in human cells. Thus, a unified stoichiometric framework provides a predictive, mechanistic account of spindle and nuclear dynamics across scales and species. Y1 - 2025 U6 - https://doi.org/10.1101/2025.10.13.681585 ER - TY - JOUR A1 - Riedmüller, Stephanie A1 - Lindner, Niels T1 - Column Generation for Periodic Timetabling N2 - Periodic timetabling for public transportation networks is typically modelled as a Periodic Event Scheduling Problem (PESP). Solving instances of the benchmark library PESPlib to optimality continues to pose a challenge. As a further approach towards this goal, we remodel the problem by a time discretization of the underlying graph and consider arc-based as well as path-based integer programming formulations. For the path-based case, we use cycles on the graph expansion of the operational lines as variables and, therefore, include more of the problem inherent structure into the model. A consequence is the validity of several known inequalities and a lower bound on the LP-relaxation, that is the best known to date. As an extension we integrate passenger routing into the new model. The proposed models have an advantage in the linear programming relaxation, on the one hand, but have an increased problem size, on the other hand. We define the corresponding pricing problems for the use of column generation to handle the size. Both models are practically tested on different problem instances. Y1 - 2025 ER - TY - JOUR A1 - Gonnermann-Müller, Jana A1 - Haase, Jennifer A1 - Fackeldey, Konstantin A1 - Pokutta, Sebastian T1 - FACET: Teacher-Centred LLM-Based Multi-Agent Systems-Towards Personalized Educational Worksheets N2 - The increasing heterogeneity of student populations poses significant challenges for teachers, particularly in mathematics education, where cognitive, motivational, and emotional differences strongly influence learning outcomes. While AI-driven personalization tools have emerged, most remain performance-focused, offering limited support for teachers and neglecting broader pedagogical needs. This paper presents the FACET framework, a teacher-facing, large language model (LLM)-based multi-agent system designed to generate individualized classroom materials that integrate both cognitive and motivational dimensions of learner profiles. The framework comprises three specialized agents: (1) learner agents that simulate diverse profiles incorporating topic proficiency and intrinsic motivation, (2) a teacher agent that adapts instructional content according to didactical principles, and (3) an evaluator agent that provides automated quality assurance. We tested the system using authentic grade 8 mathematics curriculum content and evaluated its feasibility through a) automated agent-based assessment of output quality and b) exploratory feedback from K-12 in-service teachers. Results from ten internal evaluations highlighted high stability and alignment between generated materials and learner profiles, and teacher feedback particularly highlighted structure and suitability of tasks. The findings demonstrate the potential of multi-agent LLM architectures to provide scalable, context-aware personalization in heterogeneous classroom settings, and outline directions for extending the framework to richer learner profiles and real-world classroom trials. Y1 - 2025 ER - TY - GEN ED - Fackeldey, Konstantin ED - Kannan, Aswin ED - Pokutta, Sebastian ED - Sharma, Kartikey ED - Walter, Daniel ED - Walter, Andrea ED - Weiser, Martin T1 - Mathematical Optimization for Machine Learning N2 - Mathematical optimization and machine learning are closely related. This proceedings volume of the Thematic Einstein Semester 2023 of the Berlin Mathematics Research Center MATH+ collects recent progress on their interplay in topics such as discrete optimization, nonlinear programming, optimal control, first-order methods, multilevel optimization, machine learning in optimization, physics-informed learning, and fairness in machine learning. Y1 - 2025 SN - 9783111376776 U6 - https://doi.org/10.1515/9783111376776 PB - De Gruyter ER -