@inproceedings{ZschiedrichBetzBinkowskietal.2025, author = {Zschiedrich, Lin and Betz, Fridtjof and Binkowski, Felix and Kuen, Lilli and Hammerschmidt, Martin and Burger, Sven}, title = {Efficient Photonic Component Analysis via AAA Rational Approximation}, booktitle = {2025 International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD)}, doi = {10.1109/NUSOD64393.2025.11199710}, pages = {115 -- 116}, year = {2025}, language = {en} } @inproceedings{SchemberaWuebbelingKleikampetal.2023, author = {Schembera, Bj{\"o}rn and W{\"u}bbeling, Frank and Kleikamp, Hendrik and Biedinger, Christine and Fiedler, Jochen and Reidelbach, Marco and Shehu, Aurela and Schmidt, Burkhard and Koprucki, Thomas and Iglezakis, Dotothea and G{\"o}ddeke, Dominik}, title = {Ontologies for Models and Algorithms in Applied Mathematics and Related Disciplines}, booktitle = {Metadata and Semantic Research - MTSR 2023}, edition = {Communications in Computer and Information Science}, publisher = {Springer Nature Switzerland}, address = {Cham}, arxiv = {http://arxiv.org/abs/2310.20443}, doi = {10.1007/978-3-031-65990-4_14}, pages = {161 -- 168}, year = {2023}, abstract = {In applied mathematics and related disciplines, the modeling-simulation-optimization workflow is a prominent scheme, with mathematical models and numerical algorithms playing a crucial role. For these types of mathematical research data, the Mathematical Research Data Initiative has developed, merged and implemented ontologies and knowledge graphs. This contributes to making mathematical research data FAIR by introducing semantic technology and documenting the mathematical foundations accordingly. Using the concrete example of microfracture analysis of porous media, it is shown how the knowledge of the underlying mathematical model and the corresponding numerical algorithms for its solution can be represented by the ontologies.}, language = {en} } @inproceedings{ReidelbachSchemberaWeber2024, author = {Reidelbach, Marco and Schembera, Bj{\"o}rn and Weber, Marcus}, title = {Towards a FAIR Documentation of Workflows and Models in Applied Mathematics}, volume = {14749}, booktitle = {Mathematical Software - ICMS 2024}, edition = {Lecture Notes in Computer Science}, publisher = {Springer Nature Switzerland}, address = {Cham}, arxiv = {http://arxiv.org/abs/2403.17778}, doi = {10.1007/978-3-031-64529-7_27}, pages = {254 -- 262}, year = {2024}, abstract = {Modeling-Simulation-Optimization workflows play a fundamental role in applied mathematics. The Mathematical Research Data Initiative, MaRDI, responded to this by developing a FAIR and machine-interpretable template for a comprehensive documentation of such workflows. MaRDMO, a Plugin for the Research Data Management Organiser, enables scientists from diverse fields to document and publish their workflows on the MaRDI Portal seamlessly using the MaRDI template. Central to these workflows are mathematical models. MaRDI addresses them with the MathModDB ontology, offering a structured formal model description. Here, we showcase the interaction between MaRDMO and the MathModDB Knowledge Graph through an algebraic modeling workflow from the Digital Humanities. This demonstration underscores the versatility of both services beyond their original numerical domain.}, language = {en} } @inproceedings{VaitlWinklerRichteretal.2024, author = {Vaitl, Lorenz and Winkler, Ludwig and Richter, Lorenz and Kessel, Pan}, title = {Fast and unified path gradient estimators for normalizing flows}, booktitle = {International Conference on Learning Representations 2024}, arxiv = {http://arxiv.org/abs/2403.15881}, year = {2024}, abstract = {Recent work shows that path gradient estimators for normalizing flows have lower variance compared to standard estimators for variational inference, resulting in improved training. However, they are often prohibitively more expensive from a computational point of view and cannot be applied to maximum likelihood train- ing in a scalable manner, which severely hinders their widespread adoption. In this work, we overcome these crucial limitations. Specifically, we propose a fast path gradient estimator which improves computational efficiency significantly and works for all normalizing flow architectures of practical relevance. We then show that this estimator can also be applied to maximum likelihood training for which it has a regularizing effect as it can take the form of a given target energy func- tion into account. We empirically establish its superior performance and reduced variance for several natural sciences applications.}, language = {en} } @inproceedings{LueckeKoltaiWinkelmannetal.2022, author = {L{\"u}cke, Marvin and Koltai, Peter and Winkelmann, Stefanie and Molkethin, Nora and Heitzig, Jobst}, title = {Discovering collective variable dynamics of agent-based models}, booktitle = {25th International Symposium on Mathematical Theory of Networks and Systems MTNS 2022}, doi = {https://doi.org/10.15495/EPub_UBT_00006809}, year = {2022}, abstract = {Analytical approximations of the macroscopic behavior of agent-based models (e.g. via mean-field theory) often introduce a significant error, especially in the transient phase. For an example model called continuous-time noisy voter model, we use two data-driven approaches to learn the evolution of collective variables instead. The first approach utilizes the SINDy method to approximate the macroscopic dynamics without prior knowledge, but has proven itself to be not particularly robust. The second approach employs an informed learning strategy which includes knowledge about the agent-based model. Both approaches exhibit a considerably smaller error than the conventional analytical approximation.}, language = {en} } @inproceedings{SchaibleYooWinartoetal.2024, author = {Schaible, Jonas and Yoo, Danbi and Winarto, Hanifa and Miaskiewicz, Alexandra and Albrecht, Steve and Wessels, Andreas and Bl{\"a}si, Benedikt and Burger, Sven and Jaeger, Klaus and Becker, Christiane}, title = {On aesthetical appearance of colored perovskite solar modules}, volume = {PC13014}, booktitle = {Proc. SPIE}, publisher = {SPIE}, doi = {10.1117/12.3017427}, pages = {PC1301406}, year = {2024}, language = {en} } @inproceedings{BeckerBlaesiBurgeretal.2024, author = {Becker, Christiane and Bl{\"a}si, Benedikt and Burger, Sven and Ehrler, Bruno and Gordon, Ivan and Isabella, Olindo and J{\"a}ger, Klaus and Jost, Marko and Manley, Phillip and Mu{\~n}oz, Delfina and Paetzold, Ulrich W. and Sai, Hitoshi and Yablonovitch, Eli}, title = {Optical challenges for terawatt-scale photovoltaics}, volume = {PC13014}, booktitle = {Proc. SPIE}, publisher = {SPIE}, doi = {10.1117/12.3016415}, pages = {PC1301401}, year = {2024}, language = {en} } @inproceedings{SemlerWeiser2025, author = {Semler, Phillip and Weiser, Martin}, title = {Adaptive gradient-enhanced Gaussian process surrogates for inverse problems}, booktitle = {Mathematical Optimization for Machine Learning: Proceedings of the MATH+ Thematic Einstein Semester 2023}, editor = {Fackeldey, Konstantin and Kannan, Aswin and Pokutta, Sebastian and Sharma, Kartikey and Walter, Daniel and Walther, Andrea and Weiser, Martin}, publisher = {De Gruyter}, arxiv = {http://arxiv.org/abs/2404.01864}, doi = {10.1515/9783111376776-005}, pages = {59 -- 78}, year = {2025}, abstract = {Generating simulated training data needed for constructing sufficiently accurate surrogate models to be used for efficient optimization or parameter identification can incur a huge computational effort in the offline phase. We consider a fully adaptive greedy approach to the computational design of experiments problem using gradient-enhanced Gaussian process regression as surrogates. Designs are incrementally defined by solving an optimization problem for accuracy given a certain computational budget. We address not only the choice of evaluation points but also of required simulation accuracy, both of values and gradients of the forward model. Numerical results show a significant reduction of the computational effort compared to just position-adaptive and static designs as well as a clear benefit of including gradient information into the surrogate training.}, language = {en} } @inproceedings{VillaniUngerWeiser2024, author = {Villani, Paolo and Unger, J{\"o}rg F. and Weiser, Martin}, title = {Adaptive Gaussian Process Regression for Bayesian inverse problems}, booktitle = {Proceedings of the Conference Algoritmy 2024}, pages = {214 -- 224}, year = {2024}, abstract = {We introduce a novel adaptive Gaussian Process Regression (GPR) methodology for efficient construction of surrogate models for Bayesian inverse problems with expensive forward model evaluations. An adaptive design strategy focuses on optimizing both the positioning and simulation accuracy of training data in order to reduce the computational cost of simulating training data without compromising the fidelity of the posterior distributions of parameters. The method interleaves a goal-oriented active learning algorithm selecting evaluation points and tolerances based on the expected impact on the Kullback-Leibler divergence of surrogated and true posterior with a Markov Chain Monte Carlo sampling of the posterior. The performance benefit of the adaptive approach is demonstrated for two simple test problems.}, language = {en} } @inproceedings{AndresArconesWeiserKoutsourelakisetal.2023, author = {Andr{\´e}s Arcones, Daniel and Weiser, Martin and Koutsourelakis, Faidon-Stelios and Unger, J{\"o}rg F.}, title = {Evaluation of Model Bias Identification Approaches Based on Bayesian Inference and Applications to Digital Twins}, booktitle = {5th ECCOMAS Thematic Conference on Uncertainty Quantification in Computational Sciences and Engineering}, pages = {1 -- 15}, year = {2023}, abstract = {In recent years, the use of simulation-based digital twins for monitoring and assessment of complex mechanical systems has greatly expanded. Their potential to increase the information obtained from limited data makes them an invaluable tool for a broad range of real-world applications. Nonetheless, there usually exists a discrepancy between the predicted response and the measurements of the system once built. One of the main contributors to this difference in addition to miscalibrated model parameters is the model error. Quantifying this socalled model bias (as well as proper values for the model parameters) is critical for the reliable performance of digital twins. Model bias identification is ultimately an inverse problem where information from measurements is used to update the original model. Bayesian formulations can tackle this task. Including the model bias as a parameter to be inferred enables the use of a Bayesian framework to obtain a probability distribution that represents the uncertainty between the measurements and the model. Simultaneously, this procedure can be combined with a classic parameter updating scheme to account for the trainable parameters in the original model. This study evaluates the effectiveness of different model bias identification approaches based on Bayesian inference methods. This includes more classical approaches such as direct parameter estimation using MCMC in a Bayesian setup, as well as more recent proposals such as stat-FEM or orthogonal Gaussian Processes. Their potential use in digital twins, generalization capabilities, and computational cost is extensively analyzed.}, language = {en} } @inproceedings{MukherjeeBurgerSiefkeetal.2024, author = {Mukherjee, Deshabrato and Burger, Sven and Siefke, Thomas and Gour, Jeetendra and Bodermann, Bernd and Petrik, Peter}, title = {Modeling of dimensions and sensing properties of gold gratings by spectroscopic ellipsometry and finite element method}, volume = {309}, booktitle = {EPJ Web of Conferences}, doi = {10.1051/epjconf/202430902016}, pages = {02016}, year = {2024}, language = {en} } @inproceedings{BinkowskiBetzHammerschmidtetal.2024, author = {Binkowski, Felix and Betz, Fridtjof and Hammerschmidt, Martin and Zschiedrich, Lin and Burger, Sven}, title = {Poles and Zeros in Resonant Nano-Photonic Systems}, booktitle = {2024 Conference on Lasers and Electro-Optics Pacific Rim (CLEO-PR)}, doi = {10.1109/CLEO-PR60912.2024.10676837}, year = {2024}, language = {en} } @inproceedings{JaegerBurgerAeberhardetal.2024, author = {J{\"a}ger, Klaus and Burger, Sven and Aeberhard, Urs and Llado, Esther Alarcon and Bl{\"a}si, Benedikt and Ehrler, Bruno and Favre, Wilfried and Fejfar, Anton{\´i}n and Gageot, Tristan and Gordon, Ivan and Helmers, Henning and H{\"o}hn, Oliver and Isabella, Olindo and Jošt, Marko and Ledinsk{\´y}, Martin and Mandal, Jyotirmoy and Manley, Phillip and Munoz, Delfina and Lizcano, Juan Camilo Ortiz and Paetzold, Ulrich W. and Raman, Aaswath P. and Sai, Hitoshi and Saive, Rebecca and Schmid, Martina and Yablonovitch, Eli and Becker, Christiane}, title = {A Roadmap on Optics for Terawatt Scale Photovoltaics}, booktitle = {Advanced Photonics Congress 2024}, publisher = {Optica Publishing Group}, doi = {10.1364/SELED.2024.STu2G.5}, pages = {STu2G.5}, year = {2024}, language = {en} } @inproceedings{WinklerRichterOpper2024, author = {Winkler, Ludwig and Richter, Lorenz and Opper, Manfred}, title = {Bridging discrete and continuous state spaces: Exploring the Ehrenfest process in time-continuous diffusion models}, volume = {235}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, arxiv = {http://arxiv.org/abs/2405.03549}, pages = {53017 -- 53038}, year = {2024}, abstract = {Generative modeling via stochastic processes has led to remarkable empirical results as well as to recent advances in their theoretical understanding. In principle, both space and time of the processes can be discrete or continuous. In this work, we study time-continuous Markov jump processes on discrete state spaces and investigate their correspondence to state-continuous diffusion processes given by SDEs. In particular, we revisit the Ehrenfest process, which converges to an Ornstein-Uhlenbeck process in the infinite state space limit. Likewise, we can show that the time-reversal of the Ehrenfest process converges to the time-reversed Ornstein-Uhlenbeck process. This observation bridges discrete and continuous state spaces and allows to carry over methods from one to the respective other setting. Additionally, we suggest an algorithm for training the time-reversal of Markov jump processes which relies on conditional expectations and can thus be directly related to denoising score matching. We demonstrate our methods in multiple convincing numerical experiments.}, language = {en} } @inproceedings{RiberaBorrellRichterSchuette2025, author = {Ribera Borrell, Enric and Richter, Lorenz and Sch{\"u}tte, Christof}, title = {Reinforcement Learning with Random Time Horizons}, volume = {267}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, arxiv = {http://arxiv.org/abs/2506.00962}, pages = {5101 -- 5123}, year = {2025}, language = {en} } @inproceedings{SchaibleWinartoSkorjancetal.2025, author = {Schaible, Jonas and Winarto, Hanifah and Skorjanc, Victor and Yoo, Danbi and Zimmermann, Lea and Wessels, Andreas and J{\"a}ger, Klaus and Sekulic, Ivan and Schneider, Philipp-Immanuel and Bl{\"a}si, Benedikt and Burger, Sven and Becker, Christiane}, title = {Optimization strategies for colorful thin film solar cells}, volume = {PC13361}, booktitle = {Proc. SPIE}, doi = {10.1117/12.3041408}, pages = {PC133610E}, year = {2025}, language = {en} } @inproceedings{KuenBetzBinkowskietal.2023, author = {Kuen, Lilli and Betz, Fridtjof and Binkowski, Felix and Schneider, Philipp-Immanuel and Hammerschmidt, Martin and Heermeier, Niels and Rodt, Sven and Reitzenstein, Stephan and Burger, Sven}, title = {Applying a Riesz-projection-based contour integral eigenvalue solver to compute resonance modes of a VCSEL}, volume = {12575}, booktitle = {Proc. SPIE}, arxiv = {http://arxiv.org/abs/2306.02926}, doi = {10.1117/12.2665490}, pages = {125750J}, year = {2023}, language = {en} } @inproceedings{ZinkEkteraiMartinetal.2023, author = {Zink, Christof and Ekterai, Michael and Martin, Dominik and Clemens, William and Maennel, Angela and Mundinger, Konrad and Richter, Lorenz and Crump, Paul and Knigge, Andrea}, title = {Deep-learning-based visual inspection of facets and p-sides for efficient quality control of diode lasers}, volume = {12403}, booktitle = {High-Power Diode Laser Technology XXI}, publisher = {SPIE}, doi = {10.1117/12.2648691}, pages = {94 -- 112}, year = {2023}, abstract = {The optical inspection of the surfaces of diode lasers, especially the p-sides and facets, is an essential part of the quality control in the laser fabrication procedure. With reliable, fast, and flexible optical inspection processes, it is possible to identify and eliminate defects, accelerate device selection, reduce production costs, and shorten the cycle time for product development. Due to a vast range of rapidly changing designs, structures, and coatings, however, it is impossible to realize a practical inspection with conventional software. In this work, we therefore suggest a deep learning based defect detection algorithm that builds on a Faster Regional Convolutional Neural Network (Faster R-CNN) as a core component. While for related, more general object detection problems, the application of such models is straightforward, it turns out that our task exhibits some additional challenges. On the one hand, a sophisticated pre- and postprocessing of the data has to be deployed to make the application of the deep learning model feasible. On the other hand, we find that creating labeled training data is not a trivial task in our scenario, and one has to be extra careful with model evaluation. We can demonstrate in multiple empirical assessments that our algorithm can detect defects in diode lasers accurately and reliably in most cases. We analyze the results of our production-ready pipeline in detail, discuss its limitations and provide some proposals for further improvements.}, language = {en} } @inproceedings{RichterBerner2024, author = {Richter, Lorenz and Berner, Julius}, title = {Improved sampling via learned diffusions}, booktitle = {International Conference on Learning Representations 2024}, arxiv = {http://arxiv.org/abs/2307.01198}, year = {2024}, abstract = {Recently, a series of papers proposed deep learning-based approaches to sample from unnormalized target densities using controlled diffusion processes. In this work, we identify these approaches as special cases of the Schr{\"o}dinger bridge problem, seeking the most likely stochastic evolution between a given prior distribution and the specified target. We further generalize this framework by introducing a variational formulation based on divergences between path space measures of time-reversed diffusion processes. This abstract perspective leads to practical losses that can be optimized by gradient-based algorithms and includes previous objectives as special cases. At the same time, it allows us to consider divergences other than the reverse Kullback-Leibler divergence that is known to suffer from mode collapse. In particular, we propose the so-called log-variance loss, which exhibits favorable numerical properties and leads to significantly improved performance across all considered approaches.}, language = {en} } @inproceedings{ChaukairSchuetteSunkara2023, author = {Chaukair, Mustafa and Sch{\"u}tte, Christof and Sunkara, Vikram}, title = {On the Activation Space of ReLU Equipped Deep Neural Networks}, volume = {222}, booktitle = {Procedia Computer Science}, doi = {10.1016/j.procs.2023.08.200}, pages = {624 -- 635}, year = {2023}, abstract = {Modern Deep Neural Networks are getting wider and deeper in their architecture design. However, with an increasing number of parameters the decision mechanisms becomes more opaque. Therefore, there is a need for understanding the structures arising in the hidden layers of deep neural networks. In this work, we present a new mathematical framework for describing the canonical polyhedral decomposition in the input space, and in addition, we introduce the notions of collapsing- and preserving patches, pertinent to understanding the forward map and the activation space they induce. The activation space can be seen as the output of a layer and, in the particular case of ReLU activations, we prove that this output has the structure of a polyhedral complex.}, language = {en} } @inproceedings{YousefianDonatiSikorskietal.2026, author = {Yousefian, Maryam and Donati, Luca and Sikorski, Alexander and Weber, Marcus and R{\"o}blitz, Susanna}, title = {Exploring Metastable Dynamics of Gene Regulatory Networks with ISOKANN}, volume = {15959}, booktitle = {Computational Methods in Systems Biology. CMSB 2025}, doi = {10.1007/978-3-032-01436-8_8}, pages = {126 -- 149}, year = {2026}, abstract = {Stochastic dynamical systems like gene regulatory networks (GRNs) often exhibit behavior characterized by metastable sets (representing cellular phenotypes), in which trajectories remain for long times, whereas switches between these sets in the phase space are rare events. One way to capture these rare events is to infer the system's long-term behavior from the spectral characteristics (eigenvalues and eigenvectors) of its Koopman operator. For GRNs, the Koopman operator is based on the chemical master equation (CME), which provides a precise mathematical modeling framework for stochastic GRNs. Since the CME is typically analytically intractable, methods based on discretizing the CME operator have been developed. However, determining the number and location of metastable sets in the phase space as well as the transition rates between them remains computationally challenging, especially for large GRNs with many genes and interactions. A promising alternative method, called ISOKANN (invariant subspaces of Koopman operators with artificial neural networks) has been developed in the context of molecular dynamics. ISOKANN uses a combination of the power iteration and neural networks to learn the basis functions of an invariant subspace of the Koopman operator. In this paper, we extend the application of ISOKANN to the CME operator and apply it to two small GRNs: a genetic toggle switch model and a model for macrophage polarization. Our work opens a new field of application for the ISOKANN algorithm and demonstrates the potential of this algorithm for studying large GRNs.}, language = {en} } @inproceedings{SekulicSchneiderHammerschmidtetal.2025, author = {Sekulic, Ivan and Schneider, Philipp-Immanuel and Hammerschmidt, Martin and Schaible, Jonas and Burger, Sven}, title = {Physics informed Bayesian optimization for inverse design of diffractive optical elements}, volume = {PC13573}, booktitle = {Proc. SPIE}, doi = {10.1117/12.3064372}, pages = {PC135730R}, year = {2025}, language = {en} } @inproceedings{HammerschmidtPlockBurgeretal.2025, author = {Hammerschmidt, Martin and Plock, Matthias and Burger, Sven and Truong, Vinh and Soltwisch, Victor and Schneider, Philipp-Immanuel}, title = {Machine learning approach for full Bayesian parameter reconstruction}, volume = {13568}, booktitle = {Proc. SPIE}, doi = {10.1117/12.3062268}, pages = {1356806}, year = {2025}, language = {en} } @inproceedings{BlessingBernerRichteretal.2025, author = {Blessing, Denis and Berner, Julius and Richter, Lorenz and Neumann, Gerhard}, title = {Underdamped Diffusion Bridges with Applications to Sampling}, booktitle = {13th International Conference on Learning Representations (ICLR 2025)}, arxiv = {http://arxiv.org/abs/2503.01006}, year = {2025}, abstract = {We provide a general framework for learning diffusion bridges that transport prior to target distributions. It includes existing diffusion models for generative modeling, but also underdamped versions with degenerate diffusion matrices, where the noise only acts in certain dimensions. Extending previous findings, our framework allows to rigorously show that score matching in the underdamped case is indeed equivalent to maximizing a lower bound on the likelihood. Motivated by superior convergence properties and compatibility with sophisticated numerical integration schemes of underdamped stochastic processes, we propose \emph{underdamped diffusion bridges}, where a general density evolution is learned rather than prescribed by a fixed noising process. We apply our method to the challenging task of sampling from unnormalized densities without access to samples from the target distribution. Across a diverse range of sampling problems, our approach demonstrates state-of-the-art performance, notably outperforming alternative methods, while requiring significantly fewer discretization steps and no hyperparameter tuning.}, language = {en} } @inproceedings{ChenRichterBerneretal.2025, author = {Chen, Junhua and Richter, Lorenz and Berner, Julius and Blessing, Denis and Neumann, Gerhard and Anandkumar, Anima}, title = {Sequential Controlled Langevin Diffusions}, booktitle = {13th International Conference on Learning Representations (ICLR 2025)}, arxiv = {http://arxiv.org/abs/2412.07081}, year = {2025}, abstract = {An effective approach for sampling from unnormalized densities is based on the idea of gradually transporting samples from an easy prior to the complicated target distribution. Two popular methods are (1) Sequential Monte Carlo (SMC), where the transport is performed through successive annealed densities via prescribed Markov chains and resampling steps, and (2) recently developed diffusion-based sampling methods, where a learned dynamical transport is used. Despite the common goal, both approaches have different, often complementary, advantages and drawbacks. The resampling steps in SMC allow focusing on promising regions of the space, often leading to robust performance. While the algorithm enjoys asymptotic guarantees, the lack of flexible, learnable transitions can lead to slow convergence. On the other hand, diffusion-based samplers are learned and can potentially better adapt themselves to the target at hand, yet often suffer from training instabilities. In this work, we present a principled framework for combining SMC with diffusion-based samplers by viewing both methods in continuous time and considering measures on path space. This culminates in the new Sequential Controlled Langevin Diffusion (SCLD) sampling method, which is able to utilize the benefits of both methods and reaches improved performance on multiple benchmark problems, in many cases using only 10\% of the training budget of previous diffusion-based samplers.}, language = {en} } @inproceedings{SchneiderSekulicPlocketal.2025, author = {Schneider, Philipp-Immanuel and Sekulic, Ivan and Plock, Matthias and Hammerschmidt, Martin and Rodt, Sven and Reitzenstein, Stephan and Burger, Sven}, title = {Physics-informed Bayesian optimization of nanophotonic devices}, volume = {IPRSN}, booktitle = {Advanced Photonics Congress}, doi = {10.1364/IPRSN.2025.ITu1A.2}, pages = {ITu1A.2}, year = {2025}, language = {en} } @inproceedings{JaegerMandalRandetal.2025, author = {J{\"a}ger, Klaus and Mandal, Jyotirmoy and Rand, Barry P. and Meggers, Forrest and Becker, Christiane}, title = {Thermal Management of Solar Modules with Infrared-Antireflective Coatings}, volume = {SOLITH}, booktitle = {Advanced Photonics Congress}, doi = {10.1364/SOLITH.2025.SM4E.2}, pages = {SM4E.2}, year = {2025}, language = {en} } @inproceedings{CheginiKopanicakovaWeiseretal.2022, author = {Chegini, Fatemeh and Kopanicakova, Alena and Weiser, Martin and Krause, Rolf}, title = {Quantitative Analysis of Nonlinear MultifidelityOptimization for Inverse Electrophysiology}, booktitle = {Domain Decomposition Methods in Science and Engineering XXVI}, publisher = {Springer}, pages = {65 -- 76}, year = {2022}, abstract = {The electric conductivity of cardiac tissue determines excitation propagation and is important for quantifying ischemia and scar tissue and for building personalized models. Estimating conductivity distributions from endocardial mapping data is a challenging inverse problem due to the computational complexity of the monodomain equation, which describes the cardiac excitation. For computing a maximum posterior estimate, we investigate different optimization approaches based on adjoint gradient computation: steepest descent, limited memory BFGS, and recursive multilevel trust region methods, which are using mesh hierarchies or heterogeneous model hierarchies. We compare overall performance, asymptotic convergence rate, and pre-asymptotic progress on selected examples in order to assess the benefit of our multifidelity acceleration.}, language = {en} } @inproceedings{TillmannJaegerKatzetal.2021, author = {Tillmann, Peter and J{\"a}ger, Klaus and Katz, Eugene and Becker, Christiane}, title = {Relaxed current-matching constraints by bifacial operation and luminescent coupling in perovskite/silicon tandem solar cells}, booktitle = {2021 IEEE 48th Photovoltaic Specialists Conference (PVSC)}, doi = {10.1109/PVSC43889.2021.9518891}, pages = {1086}, year = {2021}, language = {en} } @inproceedings{SchnauberSchallBounouaretal.2021, author = {Schnauber, Peter and Schall, Johannes and Bounouar, Samir and Srinivasan, Kartik and Davanco, Marcelo and Song, Jin-Dong and Burger, Sven and Rodt, Sven and Reitzenstein, Stephan}, title = {Deterministically fabricated quantum dot - waveguide systems for on-chip quantum optics}, volume = {11806}, booktitle = {Proc. SPIE}, doi = {10.1117/12.2596042}, pages = {118060G}, year = {2021}, language = {en} } @inproceedings{PerevoznikBoseBurgeretal.2021, author = {Perevoznik, Dmitrii and Bose, Surajit and Burger, Sven and Demircan, Ayhan and Morgner, Uwe}, title = {Writing Photonic Components in Polymers Using Femtosecond Pulses}, booktitle = {Conference on Lasers and Electro-Optics (CLEO)}, doi = {10.1364/CLEO_AT.2021.ATh4P.4}, pages = {ATh4P.4}, year = {2021}, language = {en} } @inproceedings{BinkowskiBetzHammerschmidtetal.2022, author = {Binkowski, Felix and Betz, Fridtjof and Hammerschmidt, Martin and Schneider, Philipp-Immanuel and Zschiedrich, Lin and Burger, Sven}, title = {Numerical methods for the investigation of resonances in nanophotonics}, booktitle = {The 12th International Conference on Metamaterials, Photonic Crystals and Plasmonics (META 2022)}, editor = {Zouhdi, S.}, issn = {2429-1390}, pages = {247}, year = {2022}, language = {en} } @inproceedings{MovsesyanSantiagoBurgeretal.2022, author = {Movsesyan, Artur and Santiago, Eva Yazmin and Burger, Sven and Correa-Duarte, Miguel and Besteiro, Lucas V. and Wang, Zhiming and Govorov, Alexander O.}, title = {The anisotropy of hot carriers' spatial distribution contrasts the isotropy of photothermal effects in complex and small plasmonic nanocrystals with complex shapes}, booktitle = {The 12th International Conference on Metamaterials, Photonic Crystals and Plasmonics (META 2022)}, editor = {Zouhdi, S.}, issn = {2429-1390}, pages = {285}, year = {2022}, language = {en} } @inproceedings{BinkowskiBetzColometal.2021, author = {Binkowski, Felix and Betz, Fridtjof and Colom, Remi and Hammerschmidt, Martin and Schneider, Philipp-Immanuel and Zschiedrich, Lin and Burger, Sven}, title = {Contour integral methods for resonance phenomena in nano-optics}, booktitle = {The 11th International Conference on Metamaterials, Photonic Crystals and Plasmonics (META 2021)}, editor = {Zouhdi, S. and Pawlak, D. and Kudelski, A.}, issn = {2429-1390}, pages = {523}, year = {2021}, language = {en} } @inproceedings{GanderKrauseWeiseretal.2023, author = {Gander, Lia and Krause, Rolf and Weiser, Martin and Costabal, Francisco and Pezzuto, Simone}, title = {On the Accuracy of Eikonal Approximations in Cardiac Electrophysiology in the Presence of Fibrosis}, volume = {13958}, booktitle = {Functional Imaging and Modeling of the Heart. FIMH 2023.}, publisher = {Springer, Cham}, doi = {10.1007/978-3-031-35302-4_14}, year = {2023}, abstract = {Fibrotic tissue is one of the main risk factors for cardiac arrhythmias. It is therefore a key component in computational studies. In this work, we compare the monodomain equation to two eikonal models for cardiac electrophysiology in the presence of fibrosis. We show that discontinuities in the conductivity field, due to the presence of fibrosis, introduce a delay in the activation times. The monodomain equation and eikonal-diffusion model correctly capture these delays, contrarily to the classical eikonal equation. Importantly, a coarse space discretization of the monodomain equation amplifies these delays, even after accounting for numerical error in conduction velocity. The numerical discretization may also introduce artificial conduction blocks and hence increase propagation complexity. Therefore, some care is required when comparing eikonal models to the discretized monodomain equation.}, language = {en} } @inproceedings{TockhornSutterCruzetal.2022, author = {Tockhorn, Philipp and Sutter, Johannes and Cruz, Alexandros and Wagner, Philipp and J{\"a}ger, Klaus and Yoo, Danbi and Lang, Felix and Grischek, Max and Li, Bor and Al-Ashouri, Amran and K{\"o}hnen, Eike and Stolterfoht, Martin and Neher, Dieter and Schlatmann, Rutger and Rech, Bernd and Stannowski, Bernd and Albrecht, Steve and Becker, Christiane}, title = {Nanooptically Enhanced Perovskite/Silicon Tandem Solar Cells with 29.80\% Power Conversion Efficiency}, booktitle = {OSA Adv. Photonics}, doi = {10.1364/NOMA.2022.JTu4H.2}, pages = {JTu4H.2}, year = {2022}, language = {en} } @inproceedings{MukherjeeKalasBurgeretal.2023, author = {Mukherjee, Deshabrato and Kalas, Benj{\´a}min and Burger, Sven and S{\´a}fr{\´a}n, Gy{\"o}rgy and Ser{\´e}nyi, Mikl{\´o}s and Fried, Mikl{\´o}s and Petrik, P{\´e}ter}, title = {Nanostructures for in-situ surface-enhanced Kretschmann-Raether ellipsometry}, volume = {12428}, booktitle = {Proc. SPIE}, arxiv = {http://arxiv.org/abs/2303.14636}, doi = {10.1117/12.2649080}, pages = {124280S}, year = {2023}, language = {en} } @inproceedings{CheginiSteinkeWeiser2022, author = {Chegini, Fatemeh and Steinke, Thomas and Weiser, Martin}, title = {Efficient adaptivity for simulating cardiac electrophysiology with spectral deferred correction methods}, arxiv = {http://arxiv.org/abs/2311.07206}, year = {2022}, abstract = {The locality of solution features in cardiac electrophysiology simulations calls for adaptive methods. Due to the overhead incurred by established mesh refinement and coarsening, however, such approaches failed in accelerating the computations. Here we investigate a different route to spatial adaptivity that is based on nested subset selection for algebraic degrees of freedom in spectral deferred correction methods. This combination of algebraic adaptivity and iterative solvers for higher order collocation time stepping realizes a multirate integration with minimal overhead. This leads to moderate but significant speedups in both monodomain and cell-by-cell models of cardiac excitation, as demonstrated at four numerical examples.}, language = {en} } @inproceedings{JaegerTillmannKatzetal.2020, author = {J{\"a}ger, Klaus and Tillmann, Peter and Katz, Eugene A. and Becker, Christiane}, title = {Simulating bifacial perovskite/silicon tandem solar cells in large PV fields}, booktitle = {OSA Advanced Photonics Congress}, doi = {10.1364/PVLED.2020.PvTh1G.3}, pages = {PvTh1G.3}, year = {2020}, language = {en} } @inproceedings{BernerRichterUllrich2024, author = {Berner, Julius and Richter, Lorenz and Ullrich, Karen}, title = {An optimal control perspective on diffusion-based generative modeling}, booktitle = {Transactions on Machine Learning Research}, year = {2024}, abstract = {We establish a connection between stochastic optimal control and generative models based on stochastic differential equations (SDEs) such as recently developed diffusion probabilistic models. In particular, we derive a Hamilton-Jacobi-Bellman equation that governs the evolution of the log-densities of the underlying SDE marginals. This perspective allows to transfer methods from optimal control theory to generative modeling. First, we show that the evidence lower bound is a direct consequence of the well-known verification theorem from control theory. Further, we develop a novel diffusion-based method for sampling from unnormalized densities -- a problem frequently occurring in statistics and computational sciences.}, language = {en} } @inproceedings{RichterBerner2022, author = {Richter, Lorenz and Berner, Julius}, title = {Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning}, volume = {162}, booktitle = {Proceedings of the 39th International Conference on Machine Learning, PMLR}, pages = {18649 -- 18666}, year = {2022}, abstract = {The combination of Monte Carlo methods and deep learning has recently led to efficient algorithms for solving partial differential equations (PDEs) in high dimensions. Related learning problems are often stated as variational formulations based on associated stochastic differential equations (SDEs), which allow the minimization of corresponding losses using gradient-based optimization methods. In respective numerical implementations it is therefore crucial to rely on adequate gradient estimators that exhibit low variance in order to reach convergence accurately and swiftly. In this article, we rigorously investigate corresponding numerical aspects that appear in the context of linear Kolmogorov PDEs. In particular, we systematically compare existing deep learning approaches and provide theoretical explanations for their performances. Subsequently, we suggest novel methods that can be shown to be more robust both theoretically and numerically, leading to substantial performance improvements.}, language = {en} } @inproceedings{SchneiderZschiedrichHammerschmidtetal.2022, author = {Schneider, Philipp-Immanuel and Zschiedrich, Lin and Hammerschmidt, Martin and Kuen, Lilli and Sekulic, Ivan and Kluge, Julien and Leykauf, Bastian and Krutzik, Markus and Burger, Sven}, title = {Boosting quantum optics experiments with Bayesian optimization}, volume = {12227}, booktitle = {Proc. SPIE}, doi = {10.1117/12.2632419}, pages = {122270F}, year = {2022}, language = {en} } @inproceedings{KruegerManleyZschiedrichetal.2022, author = {Kr{\"u}ger, Jan and Manley, Phillip and Zschiedrich, Lin and Hammerschmidt, Martin and Schneider, Philipp-Immanuel and K{\"o}ning, Rainer and Bodermann, Bernd}, title = {Untersuchung des Schwingungseinflusses auf gemessene Punktspreizfunktionen in der optischen Mikroskopie}, booktitle = {Proc. DGaO}, url = {http://nbn-resolving.de/urn:nbn:de:0287-2022-P005-6}, pages = {P5}, year = {2022}, language = {en} } @inproceedings{ManleyKruegerZschiedrichetal.2022, author = {Manley, Phillip and Kr{\"u}ger, Jan and Zschiedrich, Lin and Hammerschmidt, Martin and Bodermann, Bernd and K{\"o}ning, Rainer and Schneider, Philipp-Immanuel}, title = {Elementary, my dear Zernike: model order reduction for accelerating optical dimensional microscopy}, volume = {266}, booktitle = {EPJ Web Conf.}, doi = {10.1051/epjconf/202226610010}, pages = {10010}, year = {2022}, language = {en} } @inproceedings{SikorskiRabbenChewleetal.2025, author = {Sikorski, Alexander and Rabben, Robert Julian and Chewle, Surahit and Weber, Marcus}, title = {Capturing the Macroscopic Behaviour of Molecular Dynamics with Membership Functions}, booktitle = {Mathematical Optimization for Machine Learning: Proceedings of the MATH+ Thematic Einstein Semester 2023}, editor = {Fackeldey, K.}, publisher = {De Gruyter}, arxiv = {http://arxiv.org/abs/2404.10523}, doi = {10.1515/9783111376776-004}, pages = {41 -- 58}, year = {2025}, abstract = {Markov processes serve as foundational models in many scientific disciplines, such as molecular dynamics, and their simulation forms a common basis for analysis. While simulations produce useful trajectories, obtaining macroscopic information directly from microstate data presents significant challenges. This paper addresses this gap by introducing the concept of membership functions being the macrostates themselves. We derive equations for the holding times of these macrostates and demonstrate their consistency with the classical definition. Furthermore, we discuss the application of the ISOKANN method for learning these quantities from simulation data. In addition, we present a novel method for extracting transition paths based on the ISOKANN results and demonstrate its efficacy by applying it to simulations of the 𝜇-opioid receptor. With this approach we provide a new perspective on analyzing the macroscopic behaviour of Markov systems.}, language = {en} } @inproceedings{SteyerCheginiPotseetal.2023, author = {Steyer, Joshua and Chegini, Fatemeh and Potse, Mark and Loewe, Axel and Weiser, Martin}, title = {Continuity of Microscopic Cardiac Conduction in a Computational Cell-by-Cell Model}, volume = {50}, booktitle = {2023 Computing in Cardiology Conference (CinC)}, publisher = {Computing in Cardiology}, issn = {2325-887X}, doi = {10.22489/CinC.2023.385}, year = {2023}, abstract = {Conduction velocity in cardiac tissue is a crucial electrophysiological parameter for arrhythmia vulnerability. Pathologically reduced conduction velocity facilitates arrhythmogenesis because such conduction velocities decrease the wavelength with which re-entry may occur. Computational studies on CV and how it changes regionally in models at spatial scales multiple times larger than actual cardiac cells exist. However, microscopic conduction within cells and between them have been studied less in simulations. In this work, we study the relation of microscopic conduction patterns and clinically observable macroscopic conduction using an extracellular-membrane-intracellular model which represents cardiac tissue with these subdomains at subcellular resolution. By considering cell arrangement and non-uniform gap junction distribution, it yields anisotropic excitation propagation. This novel kind of model can for example be used to understand how discontinuous conduction on the microscopic level affects fractionation of electrograms in healthy and fibrotic tissue. Along the membrane of a cell, we observed a continuously propagating activation wavefront. When transitioning from one cell to the neighbouring one, jumps in local activation times occurred, which led to lower global conduction velocities than locally within each cell.}, language = {en} } @inproceedings{CheginiFroehlyHuynhetal.2023, author = {Chegini, Fatemeh and Froehly, Algiane and Huynh, Ngoc Mai Monica and Pavarino, Luca and Potse, Mark and Scacchi, Simone and Weiser, Martin}, title = {Efficient numerical methods for simulating cardiac electrophysiology with cellular resolution}, booktitle = {10th Int. Conf. Computational Methods for Coupled Problems in Science and Engineering 2023}, doi = {10.23967/c.coupled.2023.004}, year = {2023}, abstract = {The cardiac extracellular-membrane-intracellular (EMI) model enables the precise geometrical representation and resolution of aggregates of individual myocytes. As a result, it not only yields more accurate simulations of cardiac excitation compared to homogenized models but also presents the challenge of solving much larger problems. In this paper, we introduce recent advancements in three key areas: (i) the creation of artificial, yet realistic grids, (ii) efficient higher-order time stepping achieved by combining low-overhead spatial adaptivity on the algebraic level with progressive spectral deferred correction methods, and (iii) substructuring domain decomposition preconditioners tailored to address the complexities of heterogeneous problem structures. The efficiency gains of these proposed methods are demonstrated through numerical results on cardiac meshes of different sizes.}, language = {en} } @inproceedings{SteyerCheginiStaryetal.2024, author = {Steyer, Joshua and Chegini, Fatemeh and Star{\´y}, Tomas and Potse, Mark and Weiser, Martin and Loewe, Axel}, title = {Electrograms in a Cardiac Cell-by-Cell Model}, booktitle = {Workshop Biosignals 2024}, doi = {10.47952/gro-publ-194}, year = {2024}, abstract = {Cardiac electrograms are an important tool to study the spread of excitation waves inside the heart, which in turn underlie muscle contraction. Electrograms can be used to analyse the dynamics of these waves, e.g. in fibrotic tissue. In computational models, these analyses can be done with greater detail than during minimally invasive in vivo procedures. Whilst homogenised models have been used to study electrogram genesis, such analyses have not yet been done in cellularly resolved models. Such high resolution may be required to develop a thorough understanding of the mechanisms behind abnormal excitation patterns leading to arrhythmias. In this study, we derived electrograms from an excitation propagation simulation in the Extracellular, Membrane, Intracellular (EMI) model, which represents these three domains explicitly in the mesh. We studied the effects of the microstructural excitation dynamics on electrogram genesis and morphology. We found that electrograms are sensitive to the myocyte alignment and connectivity, which translates into micro-fractionations in the electrograms.}, language = {en} } @inproceedings{WeiserChegini2022, author = {Weiser, Martin and Chegini, Fatemeh}, title = {Adaptive multirate integration of cardiac electrophysiology with spectral deferred correction methods}, booktitle = {CMBE22 - 7th International Conference on Computational \& Mathematical Biomedical Engineering}, pages = {528 -- 531}, year = {2022}, abstract = {The highly localized dynamics of cardiac electrophysiology models call for adaptive simulation methods. Unfortunately, the overhead incurred by classical mesh adaptivity turns out to outweigh the performance improvements achieved by reducing the problem size. Here, we explore a different approach to adaptivity based on algebraic degree of freedom subset selection during spectral deferred correction sweeps, which realizes a kind of multirate higher order integration. Numerical experience indicates a significant performance increase compared to uniform simulations.}, language = {en} } @inproceedings{KostreSunkaraDjurdjevacConrad2021, author = {Kostre, Margarita and Sunkara, Vikram and Djurdjevac Conrad, Natasa}, title = {Inference of historical influence networks}, booktitle = {International Conference on Complex Networks \& Their Applications Proceedings}, edition = {International Conference on Complex Networks \& Their Applications}, doi = {10.12752/8558}, pages = {110 -- 113}, year = {2021}, abstract = {We study the romanization process of northern Africa from 50 BC till 300 AD. Our goal is to infer the communication strength between different subregions, based on the evolution of the status of cities. Herefore, we use the general inverse infection model, that infers the weights of a known underlying network, given observations of the spreading on this network. As infection process we choose the SI metapopulation model, where I stands for a city with a Roman status. To solve the minimization problem we use the particle swarm optimization algorithm with a specific choice of parameters.}, language = {de} }