TY - JOUR A1 - Gao, Timm A1 - Rickert, Lucas A1 - Urban, Felix A1 - Große, Jan A1 - Srocka, Nicole A1 - Rodt, Sven A1 - Musial, Anna A1 - Zolnacz, Kinga A1 - Mergo, Pawel A1 - Dybka, Kamil A1 - Urbanczyk, Waclaw A1 - Sek, Grzegorz A1 - Burger, Sven A1 - Reitzenstein, Stephan A1 - Heindel, Tobias T1 - A quantum key distribution testbed using a plug&play telecom-wavelength single-photon source JF - Appl. Phys. Rev. Y1 - 2022 U6 - https://doi.org/10.1063/5.0070966 VL - 9 SP - 011412 ER - TY - JOUR A1 - Torun, Cem Güney A1 - Schneider, Philipp-Immanuel A1 - Hammerschmidt, Martin A1 - Burger, Sven A1 - Munns, Joseph H.D. A1 - Schröder, Tim T1 - Optimized diamond inverted nanocones for enhanced color center to fiber coupling JF - Appl. Phys. Lett. Y1 - 2021 U6 - https://doi.org/10.1063/5.0050338 VL - 118 SP - 234002 ER - TY - JOUR A1 - Gorgulla, Christoph A1 - Çınaroğlu, Süleyman A1 - Fischer, Patrick D. A1 - Fackeldey, Konstantin A1 - Wagner, Gerhard A1 - Arthanari, Haribabu T1 - VirtualFlow Ants—Ultra-Large Virtual Screenings with Artificial Intelligence Driven Docking Algorithm Based on Ant Colony Optimization JF - Special Issue Artificial Intelligence & Deep Learning Approaches for Structural Bioinformatics N2 - The docking program PLANTS, which is based on ant colony optimization (ACO) algorithm, has many advanced features for molecular docking. Among them are multiple scoring functions, the possibility to model explicit displaceable water molecules, and the inclusion of experimental constraints. Here, we add support of PLANTS to VirtualFlow (VirtualFlow Ants), which adds a valuable method for primary virtual screenings and rescoring procedures. Furthermore, we have added support of ligand libraries in the MOL2 format, as well as on the fly conversion of ligand libraries which are in the PDBQT format to the MOL2 format to endow VirtualFlow Ants with an increased flexibility regarding the ligand libraries. The on the fly conversion is carried out with Open Babel and the program SPORES. We applied VirtualFlow Ants to a test system involving KEAP1 on the Google Cloud up to 128,000 CPUs, and the observed scaling behavior is approximately linear. Furthermore, we have adjusted several central docking parameters of PLANTS (such as the speed parameter or the number of ants) and screened 10 million compounds for each of the 10 resulting docking scenarios. We analyzed their docking scores and average docking times, which are key factors in virtual screenings. The possibility of carrying out ultra-large virtual screening with PLANTS via VirtualFlow Ants opens new avenues in computational drug discovery. Y1 - 2021 U6 - https://doi.org/https://doi.org/10.3390/ijms22115807 VL - 22 IS - 11 SP - 5807 ER - TY - JOUR A1 - Ahiboz, Doguscan A1 - Andresen, Elina A1 - Manley, Phillip A1 - Resch-Genger, Ute A1 - Würth, Christian A1 - Becker, Christiane T1 - Metasurface-Enhanced Photon Upconversion upon 1550 nm Excitation JF - Adv. Opt. Mater. Y1 - 2021 U6 - https://doi.org/10.1002/adom.202101285 VL - 9 SP - 2101285 ER - TY - JOUR A1 - Martens, Kevin A1 - Funck, Timon A1 - Santiago, Eva Y. A1 - Govorov, Alexander O. A1 - Burger, Sven A1 - Liedl, Tim T1 - Onset of Chirality in Plasmonic Meta-Molecules and Dielectric Coupling JF - ACS Nano Y1 - 2022 U6 - https://doi.org/10.1021/acsnano.2c04729 VL - 16 SP - 16143 ER - TY - CHAP A1 - Perevoznik, Dmitrii A1 - Bose, Surajit A1 - Burger, Sven A1 - Demircan, Ayhan A1 - Morgner, Uwe T1 - Photonic components in polymers made by femtosecond pulses T2 - Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) Y1 - 2021 U6 - https://doi.org/10.1109/CLEO/Europe-EQEC52157.2021.9541906 SP - 1 ER - TY - CHAP A1 - Ahiboz, Doguscan A1 - Andresen, Elina A1 - Manley, Phillip A1 - Resch Genger, Ute A1 - Würth, Christian A1 - Becker, Christiane T1 - Enhanced Photon Upconversion Using Erbium-Doped Nanoparticles Interacting with Silicon Metasurfaces T2 - IEEE 48th Photovoltaic Specialists Conference (PVSC) Y1 - 2021 U6 - https://doi.org/10.1109/PVSC43889.2021.9518495 SP - 2008 EP - 2010 ER - TY - JOUR A1 - Bittracher, Andreas A1 - Mollenhauer, Mattes A1 - Koltai, Péter A1 - Schütte, Christof T1 - Optimal Reaction Coordinates: Variational Characterization and Sparse Computation JF - Multiscale Modelling & Simulation N2 - Reaction coordinates (RCs) are indicators of hidden, low-dimensional mechanisms that govern the long-term behavior of high-dimensional stochastic processes. We present a novel and general variational characterization of optimal RCs and provide conditions for their existence. Optimal RCs are minimizers of a certain loss function, and reduced models based on them guarantee a good approximation of the statistical long-term properties of the original high-dimensional process. We show that for slow-fast systems, metastable systems, and other systems with known good RCs, the novel theory reproduces previous insight. Remarkably, for reversible systems, the numerical effort required to evaluate the loss function scales only with the variability of the underlying, low-dimensional mechanism, and not with that of the full system. The theory provided lays the foundation for an efficient and data-sparse computation of RCs via modern machine learning techniques. Y1 - 2023 U6 - https://doi.org/10.1137/21M1448367 VL - 21 IS - 2 SP - 449 EP - 488 ER - TY - JOUR A1 - Mollenhauer, Mattes A1 - Klus, Stefan A1 - Schütte, Christof A1 - Koltai, Péter T1 - Kernel Autocovariance Operators of Stationary Processes: Estimation and Convergence JF - Journal of Machine Learning Research N2 - We consider autocovariance operators of a stationary stochastic process on a Polish space that is embedded into a reproducing kernel Hilbert space. We investigate how empirical estimates of these operators converge along realizations of the process under various conditions. In particular, we examine ergodic and strongly mixing processes and obtain several asymptotic results as well as finite sample error bounds. We provide applications of our theory in terms of consistency results for kernel PCA with dependent data and the conditional mean embedding of transition probabilities. Finally, we use our approach to examine the nonparametric estimation of Markov transition operators and highlight how our theory can give a consistency analysis for a large family of spectral analysis methods including kernel-based dynamic mode decomposition. Y1 - 2022 UR - https://jmlr.org/papers/v23/20-442.html VL - 23 IS - 327 SP - 1 EP - 34 ER - TY - CHAP A1 - Rickert, Lucas A1 - Gao, Timm A1 - Urban, Felix A1 - Große, Jan A1 - Srocka, Nicole A1 - Rodt, Sven A1 - Musial, Anna A1 - Zolnacz, Kinga A1 - Mergo, Pawel A1 - Dybka, Kamil A1 - Urbanczyk, Waclaw A1 - Sek, Grzegorz A1 - Burger, Sven A1 - Reitzenstein, Stephan A1 - Tobias, Heindel T1 - A Quantum Key Distribution Testbed Using Plug&Play Telecom-Wavelength Single-Photons T2 - Quantum Information and Measurement VI Y1 - 2021 U6 - https://doi.org/10.1364/QIM.2021.M2C.1 SP - M2C.1 ER - TY - JOUR A1 - Heida, Martin A1 - Sikorski, Alexander A1 - Weber, Marcus T1 - Consistency and order 1 convergence of cell-centered finite volume discretizations of degenerate elliptic problems in any space dimension JF - SIAM Journal on Numerical Analysis N2 - We study consistency of cell-centered finite difference methods for elliptic equations with degenerate coefficients in any space dimension $d \geq 2$. This results in order of convergence estimates in the natural weighted energy norm and in the weighted discrete $L^2$-norm on admissible meshes. The cells of meshes under consideration may be very irregular in size. We particularly allow the size of certain cells to remain bounded from below even in the asymptotic limit. For uniform meshes we show that the order of convergence is at least 1 in the energy semi-norm, provided the discrete and continuous solutions exist and the continuous solution has $H^2$ regularity. Y1 - 2022 U6 - https://doi.org/10.20347/WIAS.PREPRINT.2913 ER - TY - CHAP A1 - Schneider, Philipp-Immanuel A1 - Garcia-Santiago, Xavier A1 - Wiegand, Benjamin A1 - Anton, Oliver A1 - Krutzik, Markus A1 - Rockstuhl, Carsten A1 - Burger, Sven T1 - Advances in Bayesian optimization for photonics and quantum atom optics applications T2 - OSA Advanced Photonics Congress Y1 - 2021 U6 - https://doi.org/10.1364/IPRSN.2021.JTh1E.2 SP - JTh1E.2 ER - TY - JOUR A1 - Colom, Remi A1 - Binkowski, Felix A1 - Betz, Fridtjof A1 - Kivshar, Yuri A1 - Burger, Sven T1 - Enhanced Purcell factor for nanoantennas supporting interfering resonances JF - Phys. Rev. Research Y1 - 2022 U6 - https://doi.org/10.1103/PhysRevResearch.4.023189 VL - 4 SP - 023189 ER - TY - JOUR A1 - Lücke, Marvin A1 - Nüske, Feliks T1 - tgEDMD: Approximation of the Kolmogorov Operator in Tensor Train Format JF - Journal of Nonlinear Science N2 - Extracting information about dynamical systems from models learned off simulation data has become an increasingly important research topic in the natural and engineering sciences. Modeling the Koopman operator semigroup has played a central role in this context. As the approximation quality of any such model critically depends on the basis set, recent work has focused on deriving data-efficient representations of the Koopman operator in low-rank tensor formats, enabling the use of powerful model classes while avoiding over-fitting. On the other hand, detailed information about the system at hand can be extracted from models for the infinitesimal generator, also called Kolmogorov backward operator for stochastic differential equations. In this work, we present a data-driven method to efficiently approximate the generator using the tensor train (TT) format. The centerpiece of the method is a TT representation of the tensor of generator evaluations at all data sites. We analyze consistency and complexity of the method, present extensions to practically relevant settings, and demonstrate its applicability to benchmark numerical examples. Y1 - 2022 U6 - https://doi.org/10.1007/s00332-022-09801-0 VL - 32 ER - TY - CHAP A1 - Musial, Anna A1 - Zolnacz, Kinga A1 - Srocka, Nicole A1 - Kravets, Oleh A1 - Große, Jan A1 - Schneider, Philipp-Immanuel A1 - Olszewski, Jacek A1 - Poturaj, Krzystof A1 - Wojcik, Grzegorz A1 - Mergo, Pawel A1 - Dybka, Kamil A1 - Dyrkacz, Mariusz A1 - Dlubek, Michal A1 - Rodt, Sven A1 - Burger, Sven A1 - Zschiedrich, Lin A1 - Urbanczyk, Waclaw A1 - Sek, Grzegorz A1 - Reitzenstein, Stephan T1 - Stand-alone quantum dot-based single-photon source operating at telecommunication wavelengths T2 - 10th International Conference on Spontaneous Coherence in Excitonic Systems ICSCE10 Y1 - 2020 SP - 39 ER - TY - JOUR A1 - Fackeldey, Konstantin A1 - Gorgulla, Christoph A1 - Weber, Marcus T1 - Neue Medikamente dank Supercomputern JF - Spektrum der Wissenschaft N2 - Die aktuelle Pandemie verdeutlicht, wie wichtig es ist, rasch geeignete Arzneimittel zu finden. In Computer­simulationen gelingt das erheblich schneller als im Labor. Gegen das Coronavirus ließen sich auf diese Weise bereits Wirkstoffkandidaten identifizieren. Y1 - 2021 IS - 11 SP - 40 EP - 46 ER - TY - JOUR A1 - Stout, Brian A1 - Colom, Remi A1 - Bonod, Nicolas A1 - McPhedran, Ross C T1 - Spectral expansions of open and dispersive optical systems: Gaussian regularization and convergence JF - New J. Phys. Y1 - 2021 U6 - https://doi.org/10.1088/1367-2630/ac10a6 VL - 23 SP - 083004 ER - TY - JOUR A1 - Hungenberg, Klaus-Dieter A1 - Schwede, Christian A1 - Wulkow, Michael A1 - Wulkow, Niklas T1 - Determination of reactivity ratios for acrylic acid and its dimer from classical parameter estimation and Bayesian approach JF - Canadian Journal of Chemical Engineering N2 - The reactivity ratios of acrylic acid (AA, M1) and its dimer beta-acroyloxypropionic acid (diAA, M2) are determined from cumulative copolymerization data by two different methods: classical parameter estimation (PE) by minimizing the objective function and a Bayesian analysis. Classical PE gives r1 =0.74 and r2 = 1.23 at the minimum of the residual. From the Bayesian analysis, the probability distribution of the parameter sets is obtained, revealing the existence of parameter sets with rather the same probability. The influence of the number of data and the size of the measurement error are discussed. Y1 - 2021 U6 - https://doi.org/https://doi.org/10.1002/cjce.24330 SP - 1 EP - 10 ER - TY - JOUR A1 - Driesen, Joshua A1 - El-Khatib, Ziad A1 - Wulkow, Niklas A1 - Joblin, Mitchell A1 - Vasileva, Iskriyana A1 - Glücker, Andreas A1 - Kruspel, Valentin A1 - Vogel, Catherine T1 - Data-Powered Positive Deviance during the SARS-CoV-2 Pandemic—An Ecological Pilot Study of German Districts JF - International Journal of Environmental Research and Public Health N2 - We introduced the mixed-methods Data-Powered Positive Deviance (DPPD) framework as a potential addition to the set of tools used to search for effective response strategies against the SARS-CoV-2 pandemic. For this purpose, we conducted a DPPD study in the context of the early stages of the German SARS-CoV-2 pandemic. We used a framework of scalable quantitative methods to identify positively deviant German districts that is novel in the scientific literature on DPPD, and subsequently employed qualitative methods to identify factors that might have contributed to their comparatively successful reduction of the forward transmission rate. Our qualitative analysis suggests that quick, proactive, decisive, and flexible/pragmatic actions, the willingness to take risks and deviate from standard procedures, good information flows both in terms of data collection and public communication, alongside the utilization of social network effects were deemed highly important by the interviewed districts. Our study design with its small qualitative sample constitutes an exploratory and illustrative effort and hence does not allow for a clear causal link to be established. Thus, the results cannot necessarily be extrapolated to other districts as is. However, the findings indicate areas for further research to assess these strategies’ effectiveness in a broader study setting. We conclude by stressing DPPD’s strengths regarding replicability, scalability, adaptability, as well asits focus on local solutions, which make it a promising framework to be applied in various contexts,e.g., in the context of the Global South. Y1 - 2021 U6 - https://doi.org/https://doi.org/10.3390/ijerph18189765 VL - 18 IS - 9765 SP - 1 EP - 29 ER - TY - JOUR A1 - Birk, Ralph A1 - Raharinirina, N. Alexia A1 - Fackeldey, Konstantin A1 - Richter, Tonio Sebastian A1 - Weber, Marcus T1 - Inferring cultural and social processes based on patterns of statistical relationships between Synodal texts N2 - In this paper, we explore the relationship patterns between Ancient Egyptian texts of the corpus ``Synodal decrees'', which are originating between 243 and 185 BCE, during the Ptolemaic period. Particularly, we are interested in analyzing the grammatical features of the different texts. Conventional data analysis methods such as correspondence Analysis are very useful to explore the patterns of statistical interdependence between categories of variables. However, it is based on a PCA-like dimension-reduction method and turned out to be unsuitable for our dataset due to the high dimensionality of our data representations. Additionally, the similarity between pairs of texts and pairs of grammatical features is observed through the distance between their representation, but the degree of association between a particular grammatical feature and a text is not. Here, we applied a qualitative Euclidean embedding method that provides a new Euclidean representation of the categories of variables. This new representation of the categories is constructed in such a way that all the patterns of statistical interdependence, similarity, and association, are seen through the Euclidean distance between them. Nevertheless, the PCA-like dimension-reduction method also performed poorly on our new representation. Therefore, we obtained a two-dimensional visualization using non-linear methods such UMAP or t-SNE. Although these dimension-reduction methods reduced the interpretability of interpoint distances, we were still able to identify important similarity patterns between the Synodal text as well as their association patterns with the grammatical features. Y1 - 2021 ER - TY - THES A1 - Mathew, Maria T1 - Numerical treatment of radiative heat transfer on cooling process N2 - This thesis examines how taking into account surface to surface radiation impacts the cooling process in general. We formulate the non local bound- ary condition after introducing the general setting for the cooling model. In section 3, the mathematical description of the radiative heat transfer is dis- cussed. We cover the implementation of the radiative matrix in section 4, which is followed by a brief explanation of the radiative matrix’s structure and several techniques to dealing with the accompanying challenges. We investigate the importance of radiative heat transport by applying the given approach to a two-dimensional geometry and computing the ensuing cooling curves. We compare the findings of our computation to those ac- quired from experiment conducted and find that they are extremely similar. There is a considerable difference (of about 35%) in the time of cooling of the surface where there is a possibility of influence of radiation from the second surface to that of the surface with no influence at all. Although it is possible to infer that heat convection plays a role in the total result, this has yet to be proved. However, one can clearly see the significance of the surface to surface radiative heat transfer on these parts confirming the research question posed at the begining. The effect of the surface to surface radiative heat transfer has an influence on the resulting cooling time and should be considered in the model. Y1 - 2021 ER - TY - THES A1 - Moldenhauer, Marian T1 - Adaptive Algorithms in Optimization under PDE Constraints N2 - In this thesis, adaptive algorithms in optimization under PDE constraints have been inves- tigated. In its application, the aim of optimization is to increase the longevity of implants, namely the hip joint implant, and in doing so to minimize stress shielding and simultaneously minimize the influence of locally high stresses, that, above a threshold value, are malign to the bone structure. Under the constraint of the equilibrium of forces, describing an elastodynamic setup, coupled with a contact inequality condition, a computationally expensive problem formulation is given. The first step to make the solution of the given problem possible and efficient was to change over to the spatial equilibrium equation, thus rendering an elastostatic setup. Subsequently the intrinsically dynamic motions – trajectories in the load domain – were converted to the static setup. Thus, the trajectories are marginalized to the load domain and characterized with probability distributions. Therefore the solving of the PDE constraint, the contact problem, is simplified. Yet in the whole optimization process, the solving of the PDE, the spatial equilibrium equation together with the contact condition has the most expensive contribution still and hence needed further reduction. This was achieved by application of Kriging interpolation to the load responses of the integrated distribution of stress difference and the maximum stresses. The interpolation of the two response surfaces only needs comparatively few PDE solves to set up the models. Moreover, the Kriging models can be adaptively extended by sequentially adding sample-response pairs. For this the Kriging inherent variance is used to estimate ideal new sample locations with maximum variance values. In doing so, the overall interpolation variance and therefore the interpolation error is reduced. For the integration of the integrated stress differences and penalty values on the relative high dimensional load domain Monte Carlo integration was implemented, averting the curse of dimension. Here, the motion’s probability distribution combined with patient specific data of motion frequencies is taken advantage of, making obsolete the use of the otherwise necessary importance sampling. Throughout the optimization, the FE-discretization error and the subsequently attached errors entering the solution process via PDE discretization and approximative solving of the PDE, Kriging interpolation and Monte Carlo integration need to decrease. While the FE-discretization error and the solution of the elastostatic contact problem were assumed precise enough, numerics showed, that the interpolation and integration errors can be controlled by adaptive refinement of the respective methods. For this purpose comparable error quantities for the particular algorithms were introduced and effectively put to use. For the implant position’s optimization, the derivative of the objective function was derived using the implicit function theorem. As the FE-discretization changes with implant position modifications big enough, a special line search had to be used to deal with the discontinuities in the objective function. The interplay and performance of the subalgorithms was demonstrated numerically on a reduced 2D setup of a hip joint with and without the implant. Consequently the load domain and the control variable were also limited to the 2D case. Y1 - 2021 ER - TY - JOUR A1 - Sechi, Renata A1 - Weber, Marcus A1 - Heyne, Karsten T1 - MSM building and projection for the analysis of time-resolved spectra JF - Proceedings in Applied Mathematics and Mechanics N2 - Understanding the kinetics between the components of time-resolved spectra is a crucial step in the study of photo-activatedprocesses. However, modeling the kinetics requires usually some a priori knowledge about the system. In our approach, webuild a Markov State Model (MSM) from the spectral data, and obtain a Koopman transition matrix K(t). With genPCCA,an invariant subspace projection, we project the process into its metastable components. The result of the application of gen-PCCA is a transition matrix Kc(t), from which we can read the transition probability between the metastable components of the reaction. We discuss the application of this analysis method to the transient absorption spectrum of brominated Al-corrole Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?https://onlinelibrary.wiley.com/doi/10.1002/pamm.202100102 IS - 21 SP - e202100102 ER - TY - CHAP A1 - Bopp, Julian M. A1 - Plock, Matthias A1 - Turan, Tim A1 - Pieplow, Gregor A1 - Burger, Sven A1 - Schröder, Tim T1 - ‘Sawfish’ Spin-Photon Interface for Near-Unity Emitter-to-Waveguide Coupling T2 - Conference on Lasers and Electro-Optics (CLEO) Y1 - 2023 U6 - https://doi.org/10.1364/CLEO_SI.2023.SF1O.6 SP - SF1O.6 ER - TY - CHAP A1 - Hammerschmidt, Martin A1 - Zschiedrich, Lin A1 - Siaudinyté, Lauryna A1 - Manley, Phillip A1 - Schneider, Philipp-Immanuel A1 - Burger, Sven T1 - Forward simulation of coherent beams on grating structures for coherent scatterometry T2 - Proc. SPIE Y1 - 2023 U6 - https://doi.org/10.1117/12.2673231 VL - PC12619 SP - PC1261907 ER - TY - CHAP A1 - Plock, Matthias A1 - Burger, Sven A1 - Schneider, Philipp-Immanuel T1 - Efficient reconstruction of model parameters using Bayesian target-vector optimization T2 - Proc. SPIE Y1 - 2023 U6 - https://doi.org/10.1117/12.2673590 VL - PC12619 SP - PC1261905 ER - TY - JOUR A1 - Yoo, Danbi A1 - Tillmann, Peter A1 - Krauss, Tobias A1 - Sutter, Johannes A1 - Harter, Angelika A1 - Trofimov, Sergei A1 - Naydenov, Boris A1 - Jäger, Klaus A1 - Hauser, Hubert A1 - Becker, Christiane T1 - Comparative Optical Analysis of Imprinted Nano-, Micro- and Biotextures on Solar Glasses for Increased Energy Yield JF - Solar RRL Y1 - 2023 U6 - https://doi.org/10.1002/solr.202300071 VL - 7 SP - 2300071 ER - TY - JOUR A1 - Ghysbrecht, Simon A1 - Donati, Luca A1 - Keller, Bettina G. T1 - Accuracy of reaction coordinate based rate theories for modelling chemical reactions: insights from the thermal isomerization in retinal JF - Journal of Computational Chemistry N2 - Modern potential energy surfaces have shifted attention to molecular simulations of chemical reactions. While various methods can estimate rate constants for conformational transitions in molecular dynamics simulations, their applicability to studying chemical reactions remains uncertain due to the high and sharp energy barriers and complex reaction coordinates involved. This study focuses on the thermal cis-trans isomerization in retinal, employing molecular simulations and comparing rate constant estimates based on one-dimensional rate theories with those based on sampling transitions and grid-based models for low-dimensional collective variable spaces. Even though each individual method to estimate the rate passes its quality tests, the rate constant estimates exhibit disparities of up to four orders of magnitude. Rate constant estimates based on one-dimensional reaction coordinates prove challenging to converge, even if the reaction coordinate is optimized. However, consistent estimates of the rate constant are achieved by sampling transitions and by multi-dimensional grid-based models. Y1 - 2024 U6 - https://doi.org/10.1002/jcc.27529 VL - 46 IS - 1 SP - e27529 ER - TY - JOUR A1 - Cereceda-López, Eric A1 - Ostinato, Mattia A1 - Ortiz-Ambriz, Antonio A1 - Straube, Arthur A1 - Palassini, Matteo A1 - Tierno, Pietro T1 - Excluded volume induces buckling in optically driven colloidal rings JF - Phys. Rev. Research N2 - In our combined experimental, theoretical and numerical work, we study the out of equilibrium deformations in a shrinking ring of optically trapped, interacting colloidal particles. Steerable optical tweezers are used to confine dielectric microparticles along a circle of discrete harmonic potential wells, and to reduce the ring radius at a controlled quench speed. We show that excluded-volume interactions are enough to induce particle sliding from their equilibrium positions and nonequilibrium zigzag roughening of the colloidal structure. Our work unveils the underlying mechanism of interfacial deformation in radially driven microscopic discrete rings. Y1 - 2024 U6 - https://doi.org/10.1103/PhysRevResearch.6.L012044 VL - 6 SP - L012044 ER - TY - JOUR A1 - Nagel, Soeren A1 - Heitzig, Jobst A1 - Schoell, Eckehard T1 - Macroscopic Stochastic Model for Economic Cycle Dynamics JF - Physical Review Letters N2 - We present a stochastic dynamic model which can explain economic cycles. We show that the macroscopic description yields a complex dynamical landscape consisting of multiple stable fixed points, each corresponding to a split of the population into a large low and a small high income group. The stochastic fluctuations induce switching between the resulting metastable states and excitation oscillations just below a deterministic bifurcation. The shocks are caused by the decisions of a few agents who have a disproportionate influence over the macroscopic state of the economy due to the unequal distribution of wealth among the population. The fluctuations have a long-term effect on the growth of economic output and lead to business cycle oscillations exhibiting coherence resonance, where the correlation time is controlled by the population size which is inversely proportional to the noise intensity. Y1 - 2025 U6 - https://doi.org/https://doi.org/10.1103/PhysRevLett.134.047402 VL - 134 IS - 4 SP - 1 EP - 6 PB - American Physical Society ER - TY - JOUR A1 - Anton, Oliver A1 - Henderson, Victoria A. A1 - Da Ros, Elisa A1 - Sekulic, Ivan A1 - Burger, Sven A1 - Schneider, Philipp-Immanuel A1 - Krutzik, Markus T1 - Review and experimental benchmarking of machine learning algorithms for efficient optimization of cold atom experiments JF - Mach. Learn. Sci. Technol. Y1 - 2024 U6 - https://doi.org/10.1088/2632-2153/ad3cb6 VL - 5 SP - 025022 ER - TY - THES A1 - Zonker, Johannes T1 - Coarse Graining of Agent-Based Models and Spatio-Temporal Modeling of Spreading Processes Y1 - 2023 UR - http://dx.doi.org/10.17169/refubium-41220 ER - TY - JOUR A1 - Schimunek, Johannes A1 - Seidl, Philipp A1 - Elez, Katarina A1 - Hempel, Tim A1 - Le, Tuan A1 - Noé, Frank A1 - Olsson, Simon A1 - Raich, Lluís A1 - Winter, Robin A1 - Gokcan, Hatice A1 - Gusev, Filipp A1 - Gutkin, Evgeny M. A1 - Isayev, Olexandr A1 - Kurnikova, Maria G. A1 - Narangoda, Chamali H. A1 - Zubatyuk, Roman A1 - Bosko, Ivan P. A1 - Furs, Konstantin V. A1 - Karpenko, Anna D. A1 - Kornoushenko, Yury V. A1 - Shuldau, Mikita A1 - Yushkevich, Artsemi A1 - Benabderrahmane, Mohammed B. A1 - Bousquet-Melou, Patrick A1 - Bureau, Ronan A1 - Charton, Beatrice A1 - Cirou, Bertrand C. A1 - Gil, Gérard A1 - Allen, William J. A1 - Sirimulla, Suman A1 - Watowich, Stanley A1 - Antonopoulos, Nick A1 - Epitropakis, Nikolaos A1 - Krasoulis, Agamemnon A1 - Itsikalis, Vassilis A1 - Theodorakis, Stavros A1 - Kozlovskii, Igor A1 - Maliutin, Anton A1 - Medvedev, Alexander A1 - Popov, Petr A1 - Zaretckii, Mark A1 - Eghbal-Zadeh, Hamid A1 - Halmich, Christina A1 - Hochreiter, Sepp A1 - Mayr, Andreas A1 - Ruch, Peter A1 - Widrich, Michael A1 - Berenger, Francois A1 - Kumar, Ashutosh A1 - Yamanishi, Yoshihiro A1 - Zhang, Kam Y. J. A1 - Bengio, Emmanuel A1 - Bengio, Yoshua A1 - Jain, Moksh J. A1 - Korablyov, Maksym A1 - Liu, Cheng-Hao A1 - Marcou, Gilles A1 - Glaab, Enrico A1 - Barnsley, Kelly A1 - Iyengar, Suhasini M. A1 - Ondrechen, Mary Jo A1 - Haupt, V. Joachim A1 - Kaiser, Florian A1 - Schroeder, Michael A1 - Pugliese, Luisa A1 - Albani, Simone A1 - Athanasiou, Christina A1 - Beccari, Andrea A1 - Carloni, Paolo A1 - D’Arrigo, Giulia A1 - Gianquinto, Eleonora A1 - Goßen, Jonas A1 - Hanke, Anton A1 - Joseph, Benjamin P. A1 - Kokh, Daria B. A1 - Kovachka, Sandra A1 - Manelfi, Candida A1 - Mukherjee, Goutam A1 - Muñiz-Chicharro, Abraham A1 - Musiani, Francesco A1 - Nunes-Alves, Ariane A1 - Paiardi, Giulia A1 - Rossetti, Giulia A1 - Sadiq, S. Kashif A1 - Spyrakis, Francesca A1 - Talarico, Carmine A1 - Tsengenes, Alexandros A1 - Wade, Rebecca C. A1 - Copeland, Conner A1 - Gaiser, Jeremiah A1 - Olson, Daniel R. A1 - Roy, Amitava A1 - Venkatraman, Vishwesh A1 - Wheeler, Travis J. A1 - Arthanari, Haribabu A1 - Blaschitz, Klara A1 - Cespugli, Marco A1 - Durmaz, Vedat A1 - Fackeldey, Konstantin A1 - Fischer, Patrick D. A1 - Gorgulla, Christoph A1 - Gruber, Christian A1 - Gruber, Karl A1 - Hetmann, Michael A1 - Kinney, Jamie E. A1 - Padmanabha Das, Krishna M. A1 - Pandita, Shreya A1 - Singh, Amit A1 - Steinkellner, Georg A1 - Tesseyre, Guilhem A1 - Wagner, Gerhard A1 - Wang, Zi-Fu A1 - Yust, Ryan J. A1 - Druzhilovskiy, Dmitry S. A1 - Filimonov, Dmitry A. A1 - Pogodin, Pavel V. A1 - Poroikov, Vladimir A1 - Rudik, Anastassia V. A1 - Stolbov, Leonid A. A1 - Veselovsky, Alexander V. A1 - De Rosa, Maria A1 - De Simone, Giada A1 - Gulotta, Maria R. A1 - Lombino, Jessica A1 - Mekni, Nedra A1 - Perricone, Ugo A1 - Casini, Arturo A1 - Embree, Amanda A1 - Gordon, D. Benjamin A1 - Lei, David A1 - Pratt, Katelin A1 - Voigt, Christopher A. A1 - Chen, Kuang-Yu A1 - Jacob, Yves A1 - Krischuns, Tim A1 - Lafaye, Pierre A1 - Zettor, Agnès A1 - Rodríguez, M. Luis A1 - White, Kris M. A1 - Fearon, Daren A1 - Von Delft, Frank A1 - Walsh, Martin A. A1 - Horvath, Dragos A1 - Brooks III, Charles L. A1 - Falsafi, Babak A1 - Ford, Bryan A1 - García-Sastre, Adolfo A1 - Yup Lee, Sang A1 - Naffakh, Nadia A1 - Varnek, Alexandre A1 - Klambauer, Günter A1 - Hermans, Thomas M. T1 - A community effort in SARS-CoV-2 drug discovery JF - Molecular Informatics KW - COVID-19 KW - drug discovery KW - machine learning KW - SARS-CoV-2 Y1 - 2023 U6 - https://doi.org/https://doi.org/10.1002/minf.202300262 VL - 43 IS - 1 SP - e202300262 ER - TY - JOUR A1 - Gorgulla, Christoph A1 - Nigam, AkshatKumar A1 - Koop, Matt A1 - Selim Çınaroğlu, Süleyman A1 - Secker, Christopher A1 - Haddadnia, Mohammad A1 - Kumar, Abhishek A1 - Malets, Yehor A1 - Hasson, Alexander A1 - Li, Minkai A1 - Tang, Ming A1 - Levin-Konigsberg, Roni A1 - Radchenko, Dmitry A1 - Kumar, Aditya A1 - Gehev, Minko A1 - Aquilanti, Pierre-Yves A1 - Gabb, Henry A1 - Alhossary, Amr A1 - Wagner, Gerhard A1 - Aspuru-Guzik, Alán A1 - Moroz, Yurii S. A1 - Fackeldey, Konstantin A1 - Arthanari, Haribabu T1 - VirtualFlow 2.0 - The Next Generation Drug Discovery Platform Enabling Adaptive Screens of 69 Billion Molecules JF - bioRxiv KW - preprint Y1 - 2023 U6 - https://doi.org/10.1101/2023.04.25.537981 ER - TY - CHAP A1 - Sekulic, Ivan A1 - Schneider, Philipp-Immanuel A1 - Anton, Oliver A1 - Da Ros, Elisa A1 - Henderson, Victoria A1 - Krutzik, Markus T1 - Efficient machine-learning approach to optimize trapped cold atom ensembles for quantum memory applications T2 - Proc. SPIE Y1 - 2023 U6 - https://doi.org/10.1117/12.2684406 VL - 12740 SP - 127400F ER - TY - GEN A1 - Zonker, Johannes A1 - Padilla-Iglesias, Cecilia A1 - Djurdjevac Conrad, Natasa T1 - Supplementary code and data for Royal Society Open Science Manuscript rsos.230495 N2 - In this repository are all files necessary to run the agent-based model of the paper "Insights into drivers of mobility and cultural dynamics of African hunter–gatherers over the past 120 000 years", Royal Society Open Science, 10(11), 2023. Y1 - 2023 U6 - https://doi.org/10.12752/9254 ER - TY - GEN A1 - Helfmann, Luzie A1 - Djurdjevac Conrad, Natasa A1 - Lorenz-Spreen, Philipp A1 - Schütte, Christof T1 - Supplementary code for the paper Modelling opinion dynamics under the impact of influencer and media strategies N2 - This repository contains the Julia code accompanying the paper "Modelling opinion dynamics under the impact of influencer and media strategies", Scientific Reports, Vol.13, p. 19375, 2023. Y1 - 2023 U6 - https://doi.org/10.12752/9267 ER - TY - THES A1 - Binkowski, Felix T1 - Riesz-projection-based methods for the numerical simulation of resonance phenomena in nanophotonics Y1 - 2023 U6 - https://doi.org/10.17169/refubium-41230 ER - TY - CHAP A1 - Jäger, Klaus A1 - Berwig, Sebastian A1 - Kurpiers, Jona A1 - Yang, Fengjiu A1 - Tockhorn, Philipp A1 - Albrecht, Steve A1 - Becker, Christiane T1 - Optical Simulations of Perovskite/Perovskite Tandem Solar Cells T2 - Integrated Photonics Research, Silicon and Nanophotonics Y1 - 2023 U6 - https://doi.org/10.1364/IPRSN.2023.JM4D.3 SP - JM4D.3 ER - TY - JOUR A1 - Chewle, Surahit A1 - Weber, Marcus A1 - Emmerling, Franziska T1 - Revealing Kinetics of Paracetamol Crystallization Using Time-Resolved Raman Spectroscopy, Orthogonal Time-Lapse Photography, and Non-Negative Matrix Factorization (OSANO) JF - Crystal Growth & Design N2 - Crystallization is a complex phenomenon with far-reaching implications for the production and formulation of active pharmaceutical ingredients. Understanding this process is critical for achieving control over key physicochemical properties that can affect, for example, the bioavailability and stability of a drug. In this study, we were able to reveal intricate and diverse dynamics of the formation of metastable intermediates of paracetamol crystallization varying with the choice of solvent. We demonstrate the efficacy of our novel approach utilizing an objective function-based non-negative matrix factorization technique for the analysis of time-resolved Raman spectroscopy data, in conjunction with time-lapse photography. Furthermore, we emphasize the crucial importance of integrating Raman spectroscopy with supplementary experimental instrumentation for the mathematical analysis of the obtained spectra. Y1 - 2023 U6 - https://doi.org/10.1021/acs.cgd.3c00617 VL - 23 IS - 9 SP - 6737 EP - 6746 PB - American Chemical Society ET - Crystal Growth & Design ER - TY - JOUR A1 - Oehme, Stephan A1 - Moewis, Philippe A1 - Boeth, Heide A1 - Bartek, Benjamin A1 - von Tycowicz, Christoph A1 - Ehrig, Rainald A1 - Duda, Georg A1 - Jung, Tobias T1 - Altered knee kinematics after posterior cruciate single-bundle reconstruction - a comprehensive prospective biomechanical in vivo analysis JF - Frontiers in Bioengineering and Biotechnology Y1 - 2024 U6 - https://doi.org/10.3389/fbioe.2024.1322136 VL - 12 ER - TY - CHAP A1 - Krüger, Jan A1 - Bodermann, Bernd A1 - Köning, Rainer A1 - Manley, Phillip A1 - Zschiedrich, Lin A1 - Schneider, Philipp-Immanuel A1 - Heinrich, Andreas A1 - Eder, Christian A1 - Zeiser, Ulrike A1 - Goehnermeier, Aksel T1 - On aberration retrieval for optical microscopes in length metrology T2 - Proc. SPIE Y1 - 2023 U6 - https://doi.org/10.1117/12.2672294 VL - PC12619 SP - PC126190A ER - TY - CHAP A1 - Manley, Phillip A1 - Krüger, Jan A1 - Bodermann, Bernd A1 - Köning, Rainer A1 - Heinrich, Andreas A1 - Eder, Christian A1 - Goehnermeier, Aksel A1 - Zeiser, Ulrike A1 - Hammerschmidt, Martin A1 - Zschiedrich, Lin A1 - Schneider, Philipp-Immanuel T1 - Efficient simulation of microscopic imaging for reconstruction of nanostructures T2 - Proc. SPIE Y1 - 2023 U6 - https://doi.org/10.1117/12.2673077 VL - PC12619 SP - PC126190B ER - TY - JOUR A1 - Ernst, Ariane A1 - Falkenhagen, Undine A1 - Winkelmann, Stefanie T1 - Model reduction for calcium-induced vesicle fusion dynamics JF - Proceedings in Applied Mathematics & Mechanics N2 - In this work, we adapt an established model for the Ca2+-induced fusion dynamics of synaptic vesicles and employ a lumping method to reduce its complexity. In the reduced system, sequential Ca2+-binding steps are merged to a single releasable state, while keeping the important dependence of the reaction rates on the local Ca2+ concentration. We examine the feasibility of this model reduction for a representative stimulus train over the physiologically relevant site-channel distances. Our findings show that the approximation error is generally small and exhibits an interesting nonlinear and non-monotonic behavior where it vanishes for very low distances and is insignificant at intermediary distances. Furthermore, we give expressions for the reduced model’s reaction rates and suggest that our approach may be used to directly compute effective fusion rates for assessing the validity of a fusion model, thereby circumventing expensive simulations. Y1 - 2023 U6 - https://doi.org/10.1002/pamm.202300184 VL - 23 IS - 4 ER - TY - GEN A1 - Colom, Rémi A1 - Mikheeva, Elena A1 - Achouri, Karim A1 - Zuniga-Perez, Jesus A1 - Bonod, Nicolas A1 - Martin, Olivier J.F. A1 - Burger, Sven A1 - Genevet, Patrice T1 - Crossing of the Branch Cut: The Topological Origin of a Universal 2π-Phase Retardation in Non-Hermitian Metasurfaces (Laser Photonics Rev. 17(6)/2023) T2 - Laser Photonics Rev. Y1 - 2023 U6 - https://doi.org/10.1002/lpor.202370029 VL - 17 SP - 2370029 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - JOUR A1 - Abou Hamdan, Loubnan A1 - Jana, Aloke A1 - Colom, Rémi A1 - Aboujoussef, Nour A1 - Carlson, Cooper A1 - Overvig, Adam A1 - Binkowski, Felix A1 - Burger, Sven A1 - Genevet, Patrice T1 - A Complex-Frequency Framework for Kerker Unidirectionality in Photonic Resonators Y1 - 2026 U6 - https://doi.org/10.21203/rs.3.rs-8444305/v1 ER - TY - CHAP A1 - Schembera, Björn A1 - Wübbeling, Frank A1 - Kleikamp, Hendrik A1 - Biedinger, Christine A1 - Fiedler, Jochen A1 - Reidelbach, Marco A1 - Shehu, Aurela A1 - Schmidt, Burkhard A1 - Koprucki, Thomas A1 - Iglezakis, Dotothea A1 - Göddeke, Dominik T1 - Ontologies for Models and Algorithms in Applied Mathematics and Related Disciplines T2 - Metadata and Semantic Research - MTSR 2023 N2 - 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. Y1 - 2023 U6 - https://doi.org/10.1007/978-3-031-65990-4_14 SP - 161 EP - 168 PB - Springer Nature Switzerland CY - Cham ET - Communications in Computer and Information Science ER - TY - JOUR A1 - Diederichs, Katja A1 - Krause, Celia A1 - Lemaire, Marina A1 - Reidelbach, Marco A1 - Windeck, Jürgen T1 - A Vision for Data Management Plans in the NFDI JF - ZENODO N2 - At present, data management plans (DMPs) are still often perceived as mere documents for funding agencies providing clarity on how research data will be handled during a funded project, but are not usually actively involved in the processes. However, they contain a great deal of information that can be shared automatically to facilitate active research data management (RDM) by providing metadata to research infrastructures and supporting communication between all involved stakeholders. This position paper brings together a number of ideas developed and collected during interdisciplinary workshops of the Data Management Planning Working Group (infra-dmp), which is part of the section Common Infrastructures of the National Research Data Infrastructure (NFDI) in Germany. We present our vision of a possible future role of DMPs, templates, and tools in the upcoming NFDI service architecture. Y1 - 2024 U6 - https://doi.org/10.5281/zenodo.10570653 ER - TY - CHAP A1 - Reidelbach, Marco A1 - Schembera, Björn A1 - Weber, Marcus T1 - Towards a FAIR Documentation of Workflows and Models in Applied Mathematics T2 - Mathematical Software – ICMS 2024 N2 - 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. Y1 - 2024 U6 - https://doi.org/10.1007/978-3-031-64529-7_27 VL - 14749 SP - 254 EP - 262 PB - Springer Nature Switzerland CY - Cham ET - Lecture Notes in Computer Science ER - TY - JOUR A1 - Trower, Maia A1 - Djurdjevac Conrad, Natasa A1 - Klus, Stefan T1 - Clustering Time-Evolving Networks Using the Spatiotemporal Graph Laplacian JF - Chaos: An Interdisciplinary Journal of Nonlinear Science N2 - Time-evolving graphs arise frequently when modeling complex dynamical systems such as social networks, traffic flow, and biological processes. Developing techniques to identify and analyze communities in these time-varying graph structures is an important challenge. In this work, we generalize existing spectral clustering algorithms from static to dynamic graphs using canonical correlation analysis (CCA) to capture the temporal evolution of clusters. Based on this extended canonical correlation framework, we define the spatio-temporal graph Laplacian and investigate its spectral properties. We connect these concepts to dynamical systems theory via transfer operators, and illustrate the advantages of our method on benchmark graphs by comparison with existing methods. We show that the spatio-temporal graph Laplacian allows for a clear interpretation of cluster structure evolution over time for directed and undirected graphs. Y1 - 2025 U6 - https://doi.org/10.1063/5.0228419 VL - 35 SP - 013126 ER - TY - JOUR A1 - Straube, Arthur A1 - Höfling, Felix T1 - Memory effects in colloidal motion under confinement and driving JF - J. Phys. A: Math. Theor. N2 - The transport of individual particles in inhomogeneous environments is complex and exhibits non-Markovian responses. The latter may be quantified by a memory function within the framework of the linear generalised Langevin equation (GLE). Here, we exemplify the implications of steady driving on the memory function of a colloidal model system for Brownian motion in a corrugated potential landscape, specifically, for one-dimensional motion in a sinusoidal potential. To this end, we consider the overdamped limit of the GLE, which is facilitated by separating the memory function into a singular (Markovian) and a regular (non-Markovian) part. Relying on exact solutions for the investigated model, we show that the random force entering the GLE must display a bias far from equilibrium, which corroborates a recent general prediction. Based on data for the mean-square displacement (MSD) obtained from Brownian dynamics simulations, we estimate the memory function for different driving strengths and show that already moderate driving accelerates the decay of the memory function by several orders of magnitude in time. We find that the memory may persist on much longer timescales than expected from the convergence of the MSD to its long-time asymptote. Furthermore, the functional form of the memory function changes from a monotonic decay to a non-monotonic, damped oscillatory behaviour, which can be understood from a competition of confined motion and depinning. Our analysis of the simulation data further reveals a pronounced non-Gaussianity, which questions the Gaussian approximation of the random force entering the GLE. Y1 - 2024 U6 - https://doi.org/10.1088/1751-8121/ad5b2d SN - 1751-8113 VL - 57 SP - 295003 PB - IOP Publishing ER - TY - JOUR A1 - Boniface, Dolachai A1 - Straube, Arthur A1 - Tierno, Pietro T1 - Photocatalytic magnetic microgyroscopes with activity-tunable precessional dynamics JF - Nano Letters N2 - Magnetic nano/microrotors are passive elements that spin around an axis due to an external rotating field while remaining confined to a close plane. They have been used to date in different applications related to fluid mixing, drug delivery or biomedicine. Here we realize an active version of a magnetic microgyroscope which is simultaneously driven by a photo-activated catalytic reaction and a rotating magnetic field. We investigate the uplift dynamics of this colloidal spinner when it stands up and precesses around its long axis while self-propelling due to the light induced decomposition of hydrogen peroxide in water. By combining experiments with theory, we show that activity emerging from the cooperative action of phoretic and osmotic forces effectively increase the gravitational torque which counteracts the magnetic and viscous ones, and carefully measure its contribution. Y1 - 2024 U6 - https://doi.org/10.1021/acs.nanolett.4c03386 VL - 24 IS - 47 SP - 14950 EP - 14956 ER - TY - GEN A1 - Villani, Paolo A1 - Weiser, Martin T1 - Adaptive Gaussian process regression for inverse problems. ALGORITMY 2024 N2 - This submission contains the code used for the proceedings paper for ALGORITMY 2024, concerning surrogate model-based inverse problems. Version 2 modified plot appearances and fixed some minor typos. Y1 - 2024 ER - TY - JOUR A1 - Kameda, Takeru A1 - Saha, Daniel K. A1 - Ray, Sourav A1 - Togashi, Yuichi A1 - Asano, Katsura T1 - Protocol for calculating binding free energy of RNA:RNA interactions through molecular dynamics simulations using adaptive biasing force technique JF - STAR Protocols Y1 - 2024 U6 - https://doi.org/10.1016/j.xpro.2024.103223 VL - 5 IS - 3 PB - Elsevier BV ER - TY - JOUR A1 - Richter, Lorenz A1 - Sallandt, Leon A1 - Nüsken, Nikolas T1 - From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs JF - Journal of Machine Learning Research N2 - The numerical approximation of partial differential equations (PDEs) poses formidable challenges in high dimensions since classical grid-based methods suffer from the so-called curse of dimensionality. Recent attempts rely on a combination of Monte Carlo methods and variational formulations, using neural networks for function approximation. Extending previous work (Richter et al., 2021), we argue that tensor trains provide an appealing framework for parabolic PDEs: The combination of reformulations in terms of backward stochastic differential equations and regression-type methods holds the romise of leveraging latent low-rank structures, enabling both compression and efficient computation. Emphasizing a continuous-time viewpoint, we develop iterative schemes, which differ in terms of computational efficiency and robustness. We demonstrate both theoretically and numerically that our methods can achieve a favorable trade-off between accuracy and computational efficiency. While previous methods have been either accurate or fast, we have identified a novel numerical strategy that can often combine both of these aspects. Y1 - 2024 UR - https://www.jmlr.org/papers/volume25/23-0982/23-0982.pdf VL - 25 SP - 248 ER - TY - CHAP A1 - Vaitl, Lorenz A1 - Winkler, Ludwig A1 - Richter, Lorenz A1 - Kessel, Pan T1 - Fast and unified path gradient estimators for normalizing flows T2 - International Conference on Learning Representations 2024 N2 - 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. Y1 - 2024 UR - https://openreview.net/pdf?id=zlkXLb3wpF ER - TY - CHAP A1 - Lücke, Marvin A1 - Koltai, Peter A1 - Winkelmann, Stefanie A1 - Molkethin, Nora A1 - Heitzig, Jobst T1 - Discovering collective variable dynamics of agent-based models T2 - 25th International Symposium on Mathematical Theory of Networks and Systems MTNS 2022 N2 - 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. Y1 - 2022 U6 - https://doi.org/https://doi.org/10.15495/EPub_UBT_00006809 ER - TY - JOUR A1 - Ernst, Marco A1 - Asselineau, Charles-Alexis A1 - Tillmann, Peter A1 - Jäger, Klaus A1 - Becker, Christiane T1 - Modelling bifacial irradiance – Step-by-step comparison and validation of view factor and ray tracing models JF - Appl. Energy Y1 - 2024 U6 - https://doi.org/10.1016/j.apenergy.2024.123574 SN - 0306-2619 VL - 369 SP - 123574 PB - Elsevier BV ER - TY - GEN A1 - Sekulic, Ivan A1 - Schneider, Philipp-Immanuel A1 - Hammerschmidt, Martin A1 - Burger, Sven T1 - Machine-learning driven design of metasurfaces: learn the physics and not the objective function T2 - Proc. SPIE Y1 - 2024 U6 - https://doi.org/10.1117/12.3022119 VL - PC13017 SP - PC130170X ER - TY - GEN A1 - Bopp, Julian M. A1 - Plock, Matthias A1 - Turan, Tim A1 - Pieplow, Gregor A1 - Burger, Sven A1 - Schröder, Tim T1 - ‘Sawfish’ Photonic Crystal Cavity for Near-Unity Emitter-to-Fiber Interfacing in Quantum Network Applications (Advanced Optical Materials 13/2024) T2 - Adv. Optical Mater. Y1 - 2024 U6 - https://doi.org/10.1002/adom.202470046 VL - 12 SP - 2470046 ER - TY - GEN A1 - Secker, Christopher T1 - Novel multi-objective affinity approach allows to identify pH-specific μ-opioid receptor agonists (Dataset) N2 - Virtual Screening Dataset for the paper "Novel multi-objective affinity approach allows to identify pH-specific μ-opioid receptor agonists" by Secker et al. (https://doi.org/10.1186/s13321-023-00746-4) Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-96220 ER - TY - JOUR A1 - Klus, Stefan A1 - Djurdjevac Conrad, Natasa T1 - Dynamical systems and complex networks: A Koopman operator perspective JF - Journal of Physics: Complexity N2 - The Koopman operator has entered and transformed many research areas over the last years. Although the underlying concept–representing highly nonlinear dynamical systems by infinite-dimensional linear operators–has been known for a long time, the availability of large data sets and efficient machine learning algorithms for estimating the Koopman operator from data make this framework extremely powerful and popular. Koopman operator theory allows us to gain insights into the characteristic global properties of a system without requiring detailed mathematical models. We will show how these methods can also be used to analyze complex networks and highlight relationships between Koopman operators and graph Laplacians. Y1 - 2024 U6 - https://doi.org/10.1088/2632-072X/ad9e60 VL - 5 IS - 4 SP - 041001 PB - IOP Publishing ER - TY - JOUR A1 - Sikorski, Alexander A1 - Heida, Martin T1 - Voronoi Graph - Improved raycasting and integration schemes for high dimensional Voronoi diagrams N2 - The computation of Voronoi Diagrams, or their dual Delauney triangulations is difficult in high dimensions. In a recent publication Polianskii and Pokorny propose an iterative randomized algorithm facilitating the approximation of Voronoi tesselations in high dimensions. In this paper, we provide an improved vertex search method that is not only exact but even faster than the bisection method that was previously recommended. Building on this we also provide a depth-first graph-traversal algorithm which allows us to compute the entire Voronoi diagram. This enables us to compare the outcomes with those of classical algorithms like qHull, which we either match or marginally beat in terms of computation time. We furthermore show how the raycasting algorithm naturally lends to a Monte Carlo approximation for the volume and boundary integrals of the Voronoi cells, both of which are of importance for finite Volume methods. We compare the Monte-Carlo methods to the exact polygonal integration, as well as a hybrid approximation scheme. Y1 - 2024 ER - TY - JOUR A1 - Krüger, Jan A1 - Manley, Phillip A1 - Bergmann, Detlef A1 - Köning, Rainer A1 - Bodermann, Bernd A1 - Eder, Christian A1 - Heinrich, Andreas A1 - Schneider, Philipp-Immanuel A1 - Hammerschmidt, Martin A1 - Zschiedrich, Lin A1 - Manske, Eberhard T1 - Introduction and application of a new approach for model-based optical bidirectional measurements JF - Meas. Sci. Technol. Y1 - 2024 U6 - https://doi.org/10.1088/1361-6501/ad4b53 VL - 35 SP - 085014 ER - TY - JOUR A1 - Schaible, Jonas A1 - Nouri, Bijan A1 - Höpken, Lars A1 - Kotzab, Tim A1 - Loevenich, Matthias A1 - Blum, Niklas A1 - Hammer, Annette A1 - Stührenberg, Jonas A1 - Jäger, Klaus A1 - Becker, Christiane A1 - Wilbert, Stefan T1 - Application of nowcasting to reduce the impact of irradiance ramps on PV power plants JF - EPJ Photovolt. Y1 - 2024 U6 - https://doi.org/10.1051/epjpv/2024009 VL - 15 SP - 15 ER - TY - JOUR A1 - Benner, Peter A1 - Danabalan, Renita A1 - Gödekke, Dominik A1 - Kastner, Lars A1 - Krause, Tabea A1 - Mietchen, Daniel A1 - Reidelbach, Marco A1 - Schembera, Björn A1 - Schubotz, Moritz A1 - Sinn, Rainer A1 - Tabelow, Karsten T1 - Research Data Management Planning in Mathematics N2 - Research data are crucial in mathematics and all scientific disciplines, as they form the foundation for empirical evidence, by enabling the validation and reproducibility of scientific findings. Mathematical research data (MathRD) have become vast and complex, and their interdisciplinary potential and abstract nature make them ubiquitous in various scientific fields. The volume of data and the velocity of its creation are rapidly increasing due to advancements in data science and computing power. This complexity extends to other disciplines, resulting in diverse research data and computational models. Thus, proper handling of research data is crucial both within mathematics and for its manifold connections and exchange with other disciplines. The National Research Data Infrastructure (NFDI), funded by the federal and state governments of Germany, consists of discipline-oriented consortia, including the Mathematical Research Data Initiative (MaRDI). MaRDI has been established to develop services, guidelines and outreach measures for all aspects of MathRD, and thus support the mathematical research community. Research data management (RDM) should be an integral component of every scientific project, and is becoming a mandatory component of grants with funding bodies such as the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation). At the core of RDM are the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. This document aims to guide mathematicians and researchers from related disciplines who create RDM plans. It highlights the benefits and opportunities of RDM in mathematics and interdisciplinary studies, showcases examples of diverse MathRD, and suggests technical solutions that meet the requirements of funding agencies with specific examples. The document is regularly updated to reflect the latest developments within the mathematical community represented by MaRDI. Y1 - 2023 U6 - https://doi.org/10.5281/zenodo.10018245 ER - TY - JOUR A1 - Reidelbach, Marco A1 - Ferrer, Eloi A1 - Weber, Marcus T1 - MaRDMO Plugin BT - Document and Retrieve Workflows Using the MaRDI Portal JF - Proceedings of the Conference on Research Data Infrastructure N2 - MaRDMO, a plugin for the Research Data Management Organiser, was developed in the Mathematical Research Data Initiative to document interdisciplinary workflows using a standardised scheme. Interdisciplinary workflows recorded this way are published directly on the MaRDI portal. In addition, central information is integrated into the MaRDI knowledge graph. Next to the documentation, MaRDMO offers the possibility to retrieve existing interdisciplinary workflows from the MaRDI Knowledge Graph to allow the reproduction of the initial work and to provide scientists with new researchimpulses. Thus, MaRDMO creates a community-driven knowledge loop that could help to overcome the replication crisis. Y1 - 2023 U6 - https://doi.org/10.52825/cordi.v1i.254 SN - 2941-296X VL - 1 PB - TIB Open Publishing ER - TY - JOUR A1 - Schembera, Björn A1 - Wübbeling, Frank A1 - Koprucki, Thomas A1 - Biedinger, Christine A1 - Reidelbach, Marco A1 - Schmidt, Burkhard A1 - Göddeke, Dominik A1 - Fiedler, Jochen T1 - Building Ontologies and Knowledge Graphs for Mathematics and its Applications JF - Proceedings of the Conference on Research Data Infrastructure N2 - Ontologies and knowledge graphs for mathematical algorithms and models are presented, that have been developed by the Mathematical Research Data Initiative. This enables FAIR data handling in mathematics and the applied disciplines. Moreover, challenges of harmonization during the ontology development are discussed. Y1 - 2023 U6 - https://doi.org/10.52825/cordi.v1i.255 SN - 2941-296X VL - 1 PB - TIB Open Publishing ER - TY - THES A1 - Bena, Noor-Cella T1 - Finding Rules and Patterns in Egyptological Research Objects using Boolean Polynomial Rings Y1 - 2023 ER - TY - JOUR A1 - Manley, Phillip A1 - Hammerschmidt, Martin A1 - Zschiedrich, Lin A1 - Jäger, Klaus A1 - Becker, Christiane A1 - Burger, Sven T1 - Modelling luminescent coupling in multi-junction solar cells: perovskite silicon tandem case study JF - Proc. SPIE Y1 - 2024 U6 - https://doi.org/10.1117/12.3023941 VL - 13014 SP - 1301404 ER - TY - CHAP A1 - Schaible, Jonas A1 - Yoo, Danbi A1 - Winarto, Hanifa A1 - Miaskiewicz, Alexandra A1 - Albrecht, Steve A1 - Wessels, Andreas A1 - Bläsi, Benedikt A1 - Burger, Sven A1 - Jaeger, Klaus A1 - Becker, Christiane T1 - On aesthetical appearance of colored perovskite solar modules T2 - Proc. SPIE Y1 - 2024 U6 - https://doi.org/10.1117/12.3017427 VL - PC13014 SP - PC1301406 PB - SPIE ER - TY - CHAP A1 - Becker, Christiane A1 - Bläsi, Benedikt A1 - Burger, Sven A1 - Ehrler, Bruno A1 - Gordon, Ivan A1 - Isabella, Olindo A1 - Jäger, Klaus A1 - Jost, Marko A1 - Manley, Phillip A1 - Muñoz, Delfina A1 - Paetzold, Ulrich W. A1 - Sai, Hitoshi A1 - Yablonovitch, Eli T1 - Optical challenges for terawatt-scale photovoltaics T2 - Proc. SPIE Y1 - 2024 U6 - https://doi.org/10.1117/12.3016415 VL - PC13014 SP - PC1301401 PB - SPIE ER - TY - CHAP A1 - Semler, Phillip A1 - Weiser, Martin ED - Fackeldey, Konstantin ED - Kannan, Aswin ED - Pokutta, Sebastian ED - Sharma, Kartikey ED - Walter, Daniel ED - Walther, Andrea ED - Weiser, Martin T1 - Adaptive gradient-enhanced Gaussian process surrogates for inverse problems T2 - Mathematical Optimization for Machine Learning: Proceedings of the MATH+ Thematic Einstein Semester 2023 N2 - 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. Y1 - 2025 U6 - https://doi.org/10.1515/9783111376776-005 SP - 59 EP - 78 PB - De Gruyter ER - TY - JOUR A1 - Djurdjevac Conrad, Natasa A1 - Quang Vu, Nhu A1 - Nagel, Soeren T1 - Co-evolving networks for opinion and social dynamics in agent-based models JF - Chaos: An Interdisciplinary Journal of Nonlinear Science N2 - The rise of digital social media has strengthened the coevolution of public opinions and social interactions, that shape social structures and collective outcomes in increasingly complex ways. Existing literature often explores this interplay as a one-directional influence, focusing on how opinions determine social ties within adaptive networks. However, this perspective overlooks the intrinsic dynamics driving social interactions, which can significantly influence how opinions form and evolve. In this work, we address this gap, by introducing the co-evolving opinion and social dynamics using stochastic agent-based models. Agents' mobility in a social space is governed by both their social and opinion similarity with others. Similarly, the dynamics of opinion formation is driven by the opinions of agents in their social vicinity. We analyze the underlying social and opinion interaction networks and explore the mechanisms influencing the appearance of emerging phenomena, like echo chambers and opinion consensus. To illustrate the model's potential for real-world analysis, we apply it to General Social Survey data on political identity and public opinion regarding governmental issues. Our findings highlight the model's strength in capturing the coevolution of social connections and individual opinions over time. Y1 - 2024 U6 - https://doi.org/10.1063/5.0226054 SN - 1054-1500 VL - 34 IS - 9 ER - TY - JOUR A1 - Trepte, Philipp A1 - Secker, Christopher A1 - Olivet, Julien A1 - Blavier, Jeremy A1 - Kostova, Simona A1 - Maseko, Sibusiso B A1 - Minia, Igor A1 - Silva Ramos, Eduardo A1 - Cassonnet, Patricia A1 - Golusik, Sabrina A1 - Zenkner, Martina A1 - Beetz, Stephanie A1 - Liebich, Mara J A1 - Scharek, Nadine A1 - Schütz, Anja A1 - Sperling, Marcel A1 - Lisurek, Michael A1 - Wang, Yang A1 - Spirohn, Kerstin A1 - Hao, Tong A1 - Calderwood, Michael A A1 - Hill, David E A1 - Landthaler, Markus A1 - Choi, Soon Gang A1 - Twizere, Jean-Claude A1 - Vidal, Marc A1 - Wanker, Erich E T1 - AI-guided pipeline for protein–protein interaction drug discovery identifies a SARS-CoV-2 inhibitor JF - Molecular Systems Biology N2 - Protein–protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays or AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold-Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways. KW - Applied Mathematics KW - Computational Theory and Mathematics KW - General Agricultural and Biological Sciences KW - General Immunology and Microbiology KW - General Biochemistry, Genetics and Molecular Biology KW - Information Systems Y1 - 2024 U6 - https://doi.org/https://doi.org/10.1038/s44320-024-00019-8 SN - 1744-4292 VL - 20 IS - 4 SP - 428 EP - 457 PB - Springer Science and Business Media LLC ER - TY - GEN A1 - Kuen, Lilli A1 - Löffler, Lorenz A1 - Tsarapkin, Aleksei A1 - Zschiedrich, Lin A1 - Feichtner, Thorsten A1 - Burger, Sven A1 - Höflich, Katja T1 - Source Code and Simulation Results: Chiral and directional optical emission from a dipole source coupled to a helical plasmonic antenna T2 - Zenodo Y1 - 2024 U6 - https://doi.org/10.5281/zenodo.10598255 SP - doi: 10.5281/zenodo.10598255 ER - TY - JOUR A1 - Müller, Gabriel A1 - Martínez-Lahuerta, Victor J. A1 - Sekulic, Ivan A1 - Burger, Sven A1 - Schneider, Philipp-Immanuel A1 - Gaaloul, Naceur T1 - Bayesian optimization for state engineering of quantum gases JF - Quantum Sci. Technol. Y1 - 2025 U6 - https://doi.org/10.1088/2058-9565/ad9050 VL - 10 SP - 015033 ER - TY - JOUR A1 - Erban, Radek A1 - Winkelmann, Stefanie T1 - Multi-grid reaction-diffusion master equation: applications to morphogen gradient modelling JF - Bulletin of Mathematical Biology N2 - The multi-grid reaction-diffusion master equation (mgRDME) provides a generalization of stochastic compartment-based reaction-diffusion modelling described by the standard reaction-diffusion master equation (RDME). By enabling different resolutions on lattices for biochemical species with different diffusion constants, the mgRDME approach improves both accuracy and efficiency of compartment-based reaction-diffusion simulations. The mgRDME framework is examined through its application to morphogen gradient formation in stochastic reaction-diffusion scenarios, using both an analytically tractable first-order reaction network and a model with a second-order reaction. The results obtained by the mgRDME modelling are compared with the standard RDME model and with the (more detailed) particle-based Brownian dynamics simulations. The dependence of error and numerical cost on the compartment sizes is defined and investigated through a multi-objective optimization problem. Y1 - 2025 U6 - https://doi.org/10.1007/s11538-024-01377-y VL - 87 SP - 6 ER - TY - CHAP A1 - Villani, Paolo A1 - Unger, Jörg F. A1 - Weiser, Martin T1 - Adaptive Gaussian Process Regression for Bayesian inverse problems T2 - Proceedings of the Conference Algoritmy 2024 N2 - 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. Y1 - 2024 SP - 214 EP - 224 ER - TY - JOUR A1 - Andrés Arcones, Daniel A1 - Weiser, Martin A1 - Koutsourelakis, Faidon-Stelios A1 - Unger, Jörg F. T1 - A Bayesian Framework for Simulation-based Digital Twins of Bridges JF - EUROSTRUCT 2023:European Association on Quality Control of Bridges and Structures: Digital Transformation in Sustainability N2 - Simulation-based digital twins have emerged as a powerful tool for evaluating the mechanical response of bridges. As virtual representations of physical systems, digital twins can provide a wealth of information that complements traditional inspection and monitoring data. By incorporating virtual sensors and predictive maintenance strategies, they have the potential to improve our understanding of the behavior and performance of bridges over time. However, as bridges age and undergo regular loading and extreme events, their structural characteristics change, often differing from the predictions of their initial design. Digital twins must be continuously adapted to reflect these changes. In this article, we present a Bayesian framework for updating simulation-based digital twins in the context of bridges. Our approach integrates information from measurements to account for inaccuracies in the simulation model and quantify uncertainties. Through its implementation and assessment, this work demonstrates the potential for digital twins to provide a reliable and up-to-date representation of bridge behavior, helping to inform decision-making for maintenance and management. Y1 - 2023 U6 - https://doi.org/10.1002/cepa.2177 VL - 6 IS - 5 SP - 734 EP - 740 ER - TY - CHAP A1 - Andrés Arcones, Daniel A1 - Weiser, Martin A1 - Koutsourelakis, Faidon-Stelios A1 - Unger, Jörg F. T1 - Evaluation of Model Bias Identification Approaches Based on Bayesian Inference and Applications to Digital Twins T2 - 5th ECCOMAS Thematic Conference on Uncertainty Quantification in Computational Sciences and Engineering N2 - 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. Y1 - 2023 UR - https://2023.uncecomp.org/proceedings/pdf/19795.pdf SP - 1 EP - 15 ER - TY - JOUR A1 - Maier, Kristina A1 - Weiser, Martin A1 - Conrad, Tim T1 - Hybrid PDE-ODE Models for Efficient Simulation of Infection Spread in Epidemiology JF - Proceedings of the Royal Society A N2 - This paper introduces a novel hybrid model combining Partial Differential Equations (PDEs) and Ordinary Differential Equations (ODEs) to simulate infectious disease dynamics across geographic regions. By leveraging the spatial detail of PDEs and the computational efficiency of ODEs, the model enables rapid evaluation of public health interventions. Applied to synthetic environments and real-world scenarios in Lombardy, Italy, and Berlin, Germany, the model highlights how interactions between PDE and ODE regions affect infection dynamics, especially in high-density areas. Key findings reveal that the placement of model boundaries in densely populated regions can lead to inaccuracies in infection spread, suggesting that boundaries should be positioned in areas of lower population density to better reflect transmission dynamics. Additionally, regions with low population density hinder infection flow, indicating a need for incorporating, e.g., jumps in the model to enhance its predictive capabilities. Results indicate that the hybrid model achieves a balance between computational speed and accuracy, making it a valuable tool for policymakers in real-time decision-making and scenario analysis in epidemiology and potentially in other fields requiring similar modeling approaches. Y1 - 2025 U6 - https://doi.org/10.1098/rspa.2024.0421 VL - 481 IS - 2306 PB - Royal Society ER - TY - JOUR A1 - Andrés Arcones, Daniel A1 - Weiser, Martin A1 - Koutsourelakis, Faidon-Stelios A1 - Unger, Jörg F. T1 - Model bias identification for Bayesian calibration of stochastic digital twins of bridges JF - Applied Stochastic Models in Business and Industry N2 - Simulation-based digital twins must provide accurate, robust and reliable digital representations of their physical counterparts. Quantifying the uncertainty in their predictions plays, therefore, a key role in making better-informed decisions that impact the actual system. The update of the simulation model based on data must be then carefully implemented. When applied to complex standing structures such as bridges, discrepancies between the computational model and the real system appear as model bias, which hinders the trustworthiness of the digital twin and increases its uncertainty. Classical Bayesian updating approaches aiming to infer the model parameters often fail at compensating for such model bias, leading to overconfident and unreliable predictions. In this paper, two alternative model bias identification approaches are evaluated in the context of their applicability to digital twins of bridges. A modularized version of Kennedy and O'Hagan's approach and another one based on Orthogonal Gaussian Processes are compared with the classical Bayesian inference framework in a set of representative benchmarks. Additionally, two novel extensions are proposed for such models: the inclusion of noise-aware kernels and the introduction of additional variables not present in the computational model through the bias term. The integration of such approaches in the digital twin corrects the predictions, quantifies their uncertainty, estimates noise from unknown physical sources of error and provides further insight into the system by including additional pre-existing information without modifying the computational model. Y1 - 2024 U6 - https://doi.org/10.1002/asmb.2897 VL - 41 IS - 3 ER - TY - JOUR A1 - Wehlitz, Nathalie A1 - Sadeghi, Mohsen A1 - Montefusco, Alberto A1 - Schütte, Christof A1 - Pavliotis, Grigorios A. A1 - Winkelmann, Stefanie T1 - Approximating particle-based clustering dynamics by stochastic PDEs JF - SIAM Journal on Applied Dynamical Systems N2 - This work proposes stochastic partial differential equations (SPDEs) as a practical tool to replicate clustering effects of more detailed particle-based dynamics. Inspired by membrane mediated receptor dynamics on cell surfaces, we formulate a stochastic particle-based model for diffusion and pairwise interaction of particles, leading to intriguing clustering phenomena. Employing numerical simulation and cluster detection methods, we explore the approximation of the particle-based clustering dynamics through mean-field approaches. We find that SPDEs successfully reproduce spatiotemporal clustering dynamics, not only in the initial cluster formation period, but also on longer time scales where the successive merging of clusters cannot be tracked by deterministic mean-field models. The computational efficiency of the SPDE approach allows us to generate extensive statistical data for parameter estimation in a simpler model that uses a Markov jump process to capture the temporal evolution of the cluster number. Y1 - 2025 U6 - https://doi.org/10.1137/24M1676661 VL - 24 IS - 2 SP - 1231 EP - 1250 ER - TY - JOUR A1 - Höfling, Felix A1 - Straube, Arthur T1 - Langevin equations and a geometric integration scheme for the overdamped limit of rotational Brownian motion of axisymmetric particles JF - Physical Review Research N2 - The translational motion of anisotropic or self-propelled colloidal particles is closely linked with the particle’s orientation and its rotational Brownian motion. In the overdamped limit, the stochastic evolution of the orientation vector follows a diffusion process on the unit sphere and is characterized by an orientation-dependent (“multiplicative”) noise. As a consequence, the corresponding Langevin equation attains different forms depending on whether Itō’s or Stratonovich’s stochastic calculus is used. We clarify that both forms are equivalent and derive them in a top-down approach from a geometric construction of Brownian motion on the unit sphere, based on infinitesimal random rotations. Our approach suggests further a geometric integration scheme for rotational Brownian motion, which preserves the normalization constraint of the orientation vector exactly. We show that a simple implementation of the scheme, using Gaussian random rotations, converges weakly at order 1 of the integration time step, and we outline an advanced variant of the scheme that is weakly exact for an arbitrarily large time step. Due to a favorable prefactor of the discretization error, already the Gaussian scheme allows for integration time steps that are one order of magnitude larger compared to a commonly used algorithm for rotational Brownian dynamics simulations based on projection on the constraining manifold. For torques originating from constant external fields, we prove by virtue of the Fokker-Planck equation that the constructed diffusion process satisfies detailed balance and converges to the correct equilibrium distribution. The analysis is restricted to time-homogeneous rotational Brownian motion (i.e., a single rotational diffusion constant), which is relevant for axisymmetric particles and also chemically anisotropic spheres, such as self-propelled Janus particles. Y1 - 2025 U6 - https://doi.org/10.1103/wzdn-29p4 VL - 7 SP - 043034 ER - TY - JOUR A1 - Schaible, Jonas A1 - Winarto, Hanifah A1 - Škorjanc, Viktor A1 - Yoo, Danbi A1 - Zimmermann, Lea A1 - Jäger, Klaus A1 - Sekulic, Ivan A1 - Schneider, Philipp‐Immanuel A1 - Burger, Sven A1 - Wessels, Andreas A1 - Bläsi, Benedikt A1 - Becker, Christiane T1 - Optimizing Aesthetic Appearance of Perovskite Solar Cells Using Color Filters JF - Solar RRL N2 - The significance of color aesthetics in photovoltaic (PV) modules gains importance, especially in design‐centric applications like building‐integrated PVs. Color filters based on distributed Bragg reflectors, consisting of alternating thin‐film layers of different refractive indices, can modify the appearance of standard silicon modules. This approach is also extended to optimize the color appearance of emerging PV technologies such as perovskite solar cells, which typically exhibit a less appealing gray–brownish appearance. In this contribution, perovskite solar‐cell stacks combined with MorphoColor color filters are presented. Angular‐resolved reflectance simulations based on wave optics and ray tracing with experimental data are validated, and the color appearance from various viewing angles is evaluated. Additionally, the impact of individual layers on color appearance and the maximum achievable short‐circuit current density in the perovskite solar cell is investigated. By applying Bayesian optimization, the color distance is minimized to the targeted appearance. Tailoring the bridging layers between the color filter and the perovskite solar cell is found to strongly influence the color impression due to the coherently combined color filter and perovskite solar cell. The presented color optimization concept allows to customize the aesthetics of emerging PV thin‐film technologies such as perovskite solar cells. Y1 - 2025 U6 - https://doi.org/10.1002/solr.202400627 VL - 9 SP - 2400627 PB - Wiley ER - TY - GEN A1 - Roche, Léo J. A1 - Betz, Fridtjof A1 - Yang, Yuhui A1 - Limame, Imad A1 - Shih, Ching-Wen A1 - Burger, Sven A1 - Reitzenstein, Stephan T1 - Inside Front Cover: Numerical Investigation of a Coupled Micropillar - Waveguide System for Integrated Quantum Photonic Circuits (Adv. Quantum Technol. 12/2024) T2 - Adv. Quantum Technol. Y1 - 2024 U6 - https://doi.org/10.1002/qute.202470036 VL - 7 SP - 2470036 ER - TY - CHAP A1 - Mukherjee, Deshabrato A1 - Burger, Sven A1 - Siefke, Thomas A1 - Gour, Jeetendra A1 - Bodermann, Bernd A1 - Petrik, Peter T1 - Modeling of dimensions and sensing properties of gold gratings by spectroscopic ellipsometry and finite element method T2 - EPJ Web of Conferences Y1 - 2024 U6 - https://doi.org/10.1051/epjconf/202430902016 VL - 309 SP - 02016 ER - TY - JOUR A1 - Djurdjevac Conrad, Natasa A1 - Tonello, Elisa A1 - Zonker, Johannes A1 - Siebert, Heike T1 - Detection of dynamic communities in temporal networks with sparse data JF - Applied Network Science N2 - Temporal networks are a powerful tool for studying the dynamic nature of a wide range of real-world complex systems, including social, biological and physical systems. In particular, detection of dynamic communities within these networks can help identify important cohesive structures and fundamental mechanisms driving systems behaviour. However, when working with real-world systems, available data is often limited and sparse, due to missing data on systems entities, their evolution and interactions, as well as uncertainty regarding temporal resolution. This can hinder accurate representation of the system over time and result in incomplete or biased community dynamics. In this paper, we compare established methods for community detection and, using synthetic data experiments and real-world case studies, we evaluate the impact of data sparsity on the quality of identified dynamic communities. Our results give valuable insights on the evolution of systems with sparse data, which are less studied in existing literature, but are frequently encountered in real-world applications. Y1 - 2025 U6 - https://doi.org/10.1007/s41109-024-00687-3 VL - 10 PB - Springer International Publishing ER - TY - GEN A1 - Betz, Fridtjof A1 - Hammerschmidt, Martin A1 - Zschiedrich, Lin A1 - Burger, Sven A1 - Binkowski, Felix T1 - Efficient Rational Approximation of Optical Response Functions with the AAA Algorithm (Laser Photonics Rev. 18(11)/2024) T2 - Laser Photonics Rev. Y1 - 2024 U6 - https://doi.org/10.1002/lpor.202470071 VL - 18 SP - 2470071 ER - TY - JOUR A1 - Kostré, Margarita A1 - Djurdjevac Conrad, Natasa A1 - Schütte, Christof A1 - Sunkara, Vikram T1 - Exploration of Particle Swarm Optimisation Algorithm with Divergent Parameters JF - Natural Computing Y1 - 2024 ER -