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 - CHAP A1 - Tockhorn, Philipp A1 - Sutter, Johannes A1 - Cruz, Alexandros A1 - Wagner, Philipp A1 - Jäger, Klaus A1 - Yoo, Danbi A1 - Lang, Felix A1 - Grischek, Max A1 - Li, Bor A1 - Al-Ashouri, Amran A1 - Köhnen, Eike A1 - Stolterfoht, Martin A1 - Neher, Dieter A1 - Schlatmann, Rutger A1 - Rech, Bernd A1 - Stannowski, Bernd A1 - Albrecht, Steve A1 - Becker, Christiane T1 - Nanooptically Enhanced Perovskite/Silicon Tandem Solar Cells with 29.80% Power Conversion Efficiency T2 - OSA Adv. Photonics Y1 - 2022 U6 - https://doi.org/10.1364/NOMA.2022.JTu4H.2 SP - JTu4H.2 ER - TY - CHAP A1 - Movsesyan, Artur A1 - Santiago, Eva Yazmin A1 - Burger, Sven A1 - Correa-Duarte, Miguel A1 - Besteiro, Lucas V. A1 - Wang, Zhiming A1 - Govorov, Alexander O. ED - Zouhdi, S. T1 - The anisotropy of hot carriers’ spatial distribution contrasts the isotropy of photothermal effects in complex and small plasmonic nanocrystals with complex shapes T2 - The 12th International Conference on Metamaterials, Photonic Crystals and Plasmonics (META 2022) Y1 - 2022 SN - 2429-1390 SP - 285 ER - TY - CHAP A1 - Binkowski, Felix A1 - Betz, Fridtjof A1 - Hammerschmidt, Martin A1 - Schneider, Philipp-Immanuel A1 - Zschiedrich, Lin A1 - Burger, Sven ED - Zouhdi, S. T1 - Numerical methods for the investigation of resonances in nanophotonics T2 - The 12th International Conference on Metamaterials, Photonic Crystals and Plasmonics (META 2022) Y1 - 2022 SN - 2429-1390 SP - 247 ER - TY - THES A1 - Rabben, Robert Julian T1 - Ein holistischer Ansatz zur Analyse molekularer Konformationen auf der Basis von ISOKANN Y1 - 2022 ER - TY - JOUR A1 - Fernandes, Rita A1 - Chaowdhary, Suvrat A1 - Saleh, Noureldin A1 - Mikula, Natalia A1 - Kanevche, Katerina A1 - Berlepsch, Hans A1 - Hosogi, Naoki A1 - Heberle, Joachim A1 - Weber, Marcus A1 - Böttcher, Christoph A1 - Koksch, Beate T1 - Cyanine Dye Coupling Mediates Self-assembly of a pH Sensitive Peptide into Novel 3D Architectures JF - Angewandte Chemie N2 - A conjugated Cy5 dye-peptide system reveals the formation of two novel and structurally distinct supramolecular assemblies with photo-physical characteristics of H-type dimers or tetramers, respectively. The molecular ultrastructures are triggered by the complementary interplay of mutual chromophore coupling and pH induced changes in the peptide charge pattern. Y1 - 2022 VL - 61 IS - 48 SP - e202208647 ER - TY - JOUR A1 - Montefusco, Alberto A1 - Sharma, Upanshu A1 - Tse, Oliver T1 - Fourier-Cattaneo equation: stochastic origin, variational formulation, and asymptotic limits N2 - We introduce a variational structure for the Fourier-Cattaneo (FC) system which is a second-order hyperbolic system. This variational structure is inspired by the large-deviation rate functional for the Kac process which is closely linked to the FC system. Using this variational formulation we introduce appropriate solution concepts for the FC equation and prove an a priori estimate which connects this variational structure to an appropriate Lyapunov function and Fisher information, the so-called FIR inequality. Finally, we use this formulation and estimate to study the diffusive and hyperbolic limits for the FC system. Y1 - 2022 ER - TY - GEN A1 - Weiser, Martin A1 - Chegini, Fatemeh T1 - Higher-order time integration using spectral deferred correction method (SDC) in a cell by cell discretization of cardiac excitation N2 - This C++ code implements a cell-by-cell model of cardiac excitation using a piecewise-continuous finite element discretization and spectral deferred correction time stepping. The code is based on the Kaskade 7 finite element toolbox and forms a prototype for the µCarp code to be implemented in the Microcard project. Y1 - 2022 U6 - https://doi.org/10.35097/716 ER - TY - CHAP A1 - Chegini, Fatemeh A1 - Steinke, Thomas A1 - Weiser, Martin T1 - Efficient adaptivity for simulating cardiac electrophysiology with spectral deferred correction methods N2 - 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. Y1 - 2022 ER - TY - GEN A1 - Betz, Fridtjof A1 - Binkowski, Felix A1 - Hammerschmidt, Martin A1 - Zschiedrich, Lin A1 - Burger, Sven T1 - Source code and simulation results for computing resonance expansions of quadratic quantities with regularized quasinormal modes T2 - Zenodo Y1 - 2022 U6 - https://doi.org/10.5281/zenodo.7376556 ER - TY - JOUR A1 - Krüger, Tillmann A1 - Leeners, Brigitte A1 - Tronci, Enrico A1 - Mancini, Toni A1 - Ille, Fabian A1 - Egli, Marcel A1 - Engler, Harald A1 - Röblitz, Susanna A1 - Frieling, Helge A1 - Sinke, Christopher A1 - Jahn, Kirsten T1 - The androgen system across the menstrual cycle: Hormonal, (epi-)genetic and psychometric alterations JF - Physiology and Behavior Y1 - 2022 U6 - https://doi.org/https://doi.org/10.1016/j.physbeh.2022.114034 ER - TY - JOUR A1 - Fischer-Holzhausen, Sophie A1 - Röblitz, Susanna T1 - Mathematical modelling of follicular growth and ovarian stimulation JF - Current Opinion in Endocrine and Metabolic Research Y1 - 2022 U6 - https://doi.org/https://doi.org/10.1016/j.coemr.2022.100385 ER - TY - JOUR A1 - Fischer-Holzhausen, Sophie A1 - Röblitz, Susanna T1 - Hormonal regulation of ovarian follicle growth in humans: Model-based exploration of cycle variability and parameter sensitivities JF - Journal of Theoretical Biology N2 - We present a modelling and simulation framework for the dynamics of ovarian follicles and key hormones along the hypothalamic-pituitary-gonadal axis throughout consecutive human menstrual cycles. All simulation results (hormone concentrations and ovarian follicle sizes) are in biological units and can easily be compared to clinical data. The model takes into account variability in follicles' response to stimulating hormones, which introduces variability between cycles. The growth of ovarian follicles in waves is an emergent property in our model simulations and further supports the hypothesis that follicular waves are also present in humans. We use Approximate Bayesian Computation and cluster analysis to construct a population of virtual subjects and to study parameter distributions and sensitivities. The model can be used to compare and optimize treatment protocols for ovarian hyperstimulation, thus potentially forming the integral part of a clinical decision support system in reproductive endocrinology. Y1 - 2022 U6 - https://doi.org/https://doi.org/10.1016/j.jtbi.2022.111150 ER - TY - THES A1 - Chegini, Fatemeh T1 - Multilevel optimization algorithm for Inverse Problem in Electrocardiography N2 - The electric conductivity of cardiac tissue determines excitation propagation and is vital for quantifying ischemia and scar tissue and building personalized models. As scar tissue is generally characterized by different conduction of electrical excitation, we aim to estimate conductivity-related parameters in mathematical excitation models from endocardial mapping data, particularly the anisotropic conductivity tensor in the monodomain equation, which describes the cardiac excitation. Yet, estimating the distributed and anisotropic conductivity tensors reliably and efficiently from endocardial mapping data or electrocardiograms is a challenging inverse problem due to the computational complexity of the monodomain equation; Many expensive high-resolution computations for the monodomain equation on very fine space and time discretizations are involved. Thus, we aim at building an efficient multilevel method for accelerating the estimation procedure combining electrophysiology models of different complex- ity, which uses a computationally cheap eikonal model in addition to the more accurate monodomain model. Distributed parameter estimation, well-known as an ill-posed inverse problem, can be performed by minimizing the misfit between simulated and measured electrical activity on the endocardial surface subject to the monodomain model and some regularization, leading to a partial differential equation constrained optimization problem. We formulate this optimization problem, including scar tissue modeling and different regularizations, and design an efficient iterative solver. To this aim, we consider monodomain grid hi- erarchies, monodomain-eikonal model hierarchies, and the combination of both hierarchies in a recursive multilevel trust-region (RMTR) method. On the one hand, both the trust region method’s estimation quality and efficiency, independent of the data, are investigated from several numerical exam- ples. Endocardial mapping data of realistic density appears to be sufficient to provide quantitatively reasonable estimates of the location, size, and shape of scars close to the endocardial surface. In several situations, scar reconstruction based on eikonal and monodomain models differ significantly, suggesting the use of the more involved monodomain model for this purpose. Moreover, Eikonal models can accelerate the computations considerably, enabling the use of complex electrophysiology models for estimating myocardial scars from endocardial mapping data. In many situations, eikonal models approximate monodomain models well but are orders of magnitude faster to solve. Thus, eikonal models can utilize them to provide an RMTR acceleration with negligible overhead per iteration, resulting in a practical approach to estimating myocardial scars from endocardial mapping data. In addition, the multilevel solver is faster than a comparable single-level solver. On the other hand, we investigate different optimization approaches based on adjoint gradient computation for computing a maximum posterior estimate: steepest descent, limited memory BFGS, and recursive multilevel trust region methods 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. Y1 - 2022 ER - TY - GEN A1 - Rickert, Lucas A1 - Betz, Fridtjof A1 - Plock, Matthias A1 - Burger, Sven A1 - Heindel, Tobias T1 - Data publication for "High-performance designs for fiber-pigtailed quantum-light sources based on quantum dots in electrically-controlled circular Bragg gratings" T2 - Zenodo Y1 - 2022 U6 - https://doi.org/10.5281/zenodo.7360516 SP - 7360516 ER - TY - CHAP A1 - Richter, Lorenz A1 - Berner, Julius T1 - Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning T2 - Proceedings of the 39th International Conference on Machine Learning, PMLR N2 - 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. Y1 - 2022 VL - 162 SP - 18649 EP - 18666 ER - TY - CHAP A1 - Borndörfer, Ralf A1 - Danecker, Fabian A1 - Weiser, Martin T1 - A Discrete-Continuous Algorithm for Globally Optimal Free Flight Trajectory Optimization T2 - 22nd Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2022) N2 - We present an efficient algorithm that finds a globally optimal solution to the 2D Free Flight Trajectory Optimization Problem (aka Zermelo Navigation Problem) up to arbitrary precision in finite time. The algorithm combines a discrete and a continuous optimization phase. In the discrete phase, a set of candidate paths that densely covers the trajectory space is created on a directed auxiliary graph. Then Yen’s algorithm provides a promising set of discrete candidate paths which subsequently undergo a locally convergent refinement stage. Provided that the auxiliary graph is sufficiently dense, the method finds a path that lies within the convex domain around the global minimizer. From this starting point, the second stage will converge rapidly to the optimum. The density of the auxiliary graph depends solely on the wind field, and not on the accuracy of the solution, such that the method inherits the superior asymptotic convergence properties of the optimal control stage. Y1 - 2022 U6 - https://doi.org/10.4230/OASIcs.ATMOS.2022.2 VL - 106 SP - 1 EP - 13 ER - TY - GEN A1 - Reidelbach, Marco A1 - Weber, Marcus T1 - MaRDI - The mathematical Research Data Initiative T2 - Aktionstag Forschungsdaten Y1 - 2022 U6 - https://doi.org/10.5281/zenodo.7397588 ER - TY - JOUR A1 - Reidelbach, Marco A1 - Bai, Mei A1 - Zöllner, Martin Sebastian A1 - Schneeberger, Michaela A1 - Kubicek, Katharina A1 - Kirchberg, Henning A1 - Bressler, Christian A1 - Thorwart, Michael A1 - Herrmann, Carmen T1 - Solvent dynamics of aqueous halides before and after photoionization JF - Journal of Physical Chemistry Y1 - 2022 ER - TY - JOUR A1 - Raharinirina, N. Alexia A1 - Fackeldey, Konstantin A1 - Weber, Marcus T1 - Qualitative Euclidean embedding of Disjoint Sets of Points N2 - We consider two disjoint sets of points with a distance metric, or a proximity function, associated with each set. If each set can be separately embedded into separate Euclidean spaces, then we provide sufficient conditions for the two sets to be jointly embedded in one Euclidean space. In this joint Euclidean embedding, the distances between the points are generated by a specific relation-preserving function. Consequently, the mutual distances between two points of the same set are specific qualitative transformations of their mutual distances in their original space; the pairwise distances between the points of different sets can be constructed from an arbitrary proximity function (might require scaling). Y1 - 2022 UR - https://arxiv.org/abs/2212.00058 ER - TY - GEN A1 - Schütte, Christof A1 - Klus, Stefan A1 - Hartmann, Carsten T1 - Overcoming the Timescale Barrier in Molecular Dynamics: Transfer Operators, Variational Principles, and Machine Learning N2 - One of the main challenges in molecular dynamics is overcoming the “timescale barrier”, a phrase used to describe that in many realistic molecular systems, biologically important rare transitions occur on timescales that are not accessible to direct numerical simulation, not even on the largest or specifically dedicated supercomputers. This article discusses how to circumvent the timescale barrier by a collection of transfer operator-based techniques that have emerged from dynamical systems theory, numerical mathematics, and machine learning over the last two decades. We will focus on how transfer operators can be used to approximate the dynamical behavior on long timescales, review the introduction of this approach into molecular dynamics, and outline the respective theory as well as the algorithmic development from the early numerics-based methods, via variational reformulations, to modern data-based techniques utilizing and improving concepts from machine learning. Furthermore, its relation to rare event simulation techniques will be explained, revealing a broad equivalence of variational principles for long-time quantities in MD. The article will mainly take a mathematical perspective and will leave the application to real-world molecular systems to the more than 1000 research articles already written on this subject. T3 - ZIB-Report - 22-25 Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-88637 SN - 1438-0064 ER - TY - JOUR A1 - Raharinirina, N. Alexia A1 - Acevedo-Trejos, Esteban A1 - Merico, Agostino T1 - Modelling the acclimation capacity of coral reefs to a warming ocean JF - PLOS COMPUTATIONAL BIOLOGY N2 - The symbiotic relationship between corals and photosynthetic algae is the foundation of coral reef ecosystems. This relationship breaks down, leading to coral death, when sea temperature exceeds the thermal tolerance of the coral-algae complex. While acclimation via phenotypic plasticity at the organismal level is an important mechanism for corals to cope with global warming, community-based shifts in response to acclimating capacities may give valuable indications about the future of corals at a regional scale. Reliable regional-scale predictions, however, are hampered by uncertainties on the speed with which coral communities will be able to acclimate. Here we present a trait-based, acclimation dynamics model, which we use in combination with observational data, to provide a first, crude estimate of the speed of coral acclimation at the community level and to investigate the effects of different global warming scenarios on three iconic reef ecosystems of the tropics: Great Barrier Reef, South East Asia, and Caribbean. The model predicts that coral acclimation may confer some level of protection by delaying the decline of some reefs such as the Great Barrier Reef. However, the current rates of acclimation will not be sufficient to rescue corals from global warming. Based on our estimates of coral acclimation capacities, the model results suggest substantial declines in coral abundances in all three regions, ranging from 12% to 55%, depending on the region and on the climate change scenario considered. Our results highlight the importance and urgency of precise assessments and quantitative estimates, for example through laboratory experiments, of the natural acclimation capacity of corals and of the speed with which corals may be able to acclimate to global warming. Y1 - 2022 U6 - https://doi.org/10.1371/journal.pcbi.1010099 ER - TY - JOUR A1 - Donati, Luca A1 - Weber, Marcus A1 - Keller, Bettina G. T1 - A review of Girsanov Reweighting and of Square Root Approximation for building molecular Markov State Models JF - Journal of Mathematical Physics N2 - Dynamical reweighting methods permit to estimate kinetic observables of a stochastic process governed by a target potential U(x) from trajectories that have been generated at a different potential V(x). In this article, we present Girsanov reweighting and Square Root Approximation (SqRA): the first method reweights path probabilities exploiting the Girsanov theorem and can be applied to Markov State Models (MSMs) to reweight transition probabilities; the second method was originally developed to discretize the Fokker-Planck operator into a transition rate matrix, but here we implement it into a reweighting scheme for transition rates. We begin by reviewing the theoretical background of the methods, then present two applications relevant to Molecular Dynamics (MD), highlighting their strengths and weaknesses. Y1 - 2022 U6 - https://doi.org/10.1063/5.0127227 VL - 63 IS - 12 SP - 123306-1 EP - 123306-21 PB - AIP Publishing ER - TY - JOUR A1 - Djurdjevac Conrad, Natasa A1 - Köppl, Jonas A1 - Djurdjevac, Ana T1 - Feedback Loops in Opinion Dynamics of Agent-Based Models with Multiplicative Noise JF - Entropy N2 - We introduce an agent-based model for co-evolving opinions and social dynamics, under the influence of multiplicative noise. In this model, every agent is characterized by a position in a social space and a continuous opinion state variable. Agents’ movements are governed by the positions and opinions of other agents and similarly, the opinion dynamics are influenced by agents’ spatial proximity and their opinion similarity. Using numerical simulations and formal analyses, we study this feedback loop between opinion dynamics and the mobility of agents in a social space. We investigate the behaviour of this ABM in different regimes and explore the influence of various factors on the appearance of emerging phenomena such as group formation and opinion consensus. We study the empirical distribution, and, in the limit of infinite number of agents, we derive a corresponding reduced model given by a partial differential equation (PDE). Finally, using numerical examples, we show that a resulting PDE model is a good approximation of the original ABM. Y1 - 2022 U6 - https://doi.org/10.3390/e24101352 VL - 24(10) ER - TY - JOUR A1 - Donati, Luca A1 - Weber, Marcus T1 - Assessing transition rates as functions of environmental variables JF - The Journal of Chemical Physics N2 - We present a method to estimate the transition rates of molecular systems under different environmental conditions which cause the formation or the breaking of bonds and require the sampling of the Grand Canonical Ensemble. For this purpose, we model the molecular system in terms of probable "scenarios", governed by different potential energy functions, which are separately sampled by classical MD simulations. Reweighting the canonical distribution of each scenario according to specific environmental variables, we estimate the grand canonical distribution, then we use the Square Root Approximation (SqRA) method to discretize the Fokker-Planck operator into a rate matrix and the robust Perron Cluster Cluster Analysis (PCCA+) method to coarse-grain the kinetic model. This permits to efficiently estimate the transition rates of conformational states as functions of environmental variables, for example, the local pH at a cell membrane. In this work we formalize the theoretical framework of the procedure and we present a numerical experiment comparing the results with those provided by a constant-pH method based on non-equilibrium Molecular Dynamics Monte Carlo simulations. The method is relevant for the development of new drug design strategies which take into account how the cellular environment influences biochemical processes. Y1 - 2022 U6 - https://doi.org/10.1063/5.0109555 VL - 157 IS - 22 SP - 224103-1 EP - 224103-14 PB - AIP Publishing ER - TY - GEN A1 - Straube, Arthur A1 - Winkelmann, Stefanie A1 - Höfling, Felix T1 - Accurate reduced models for the pH oscillations in the urea-urease reaction confined to giant lipid vesicles N2 - Our theoretical study concerns an urea-urease-based pH oscillator confined to giant lipid vesicles. Under suitable conditions, differential transport of urea and hydrogen ion across the unilamellar vesicle membrane periodically resets the pH clock that switches the system from acid to basic, resulting in self-sustained oscillations. We analyse the structure of the limit cycle, which controls the dynamics for giant vesicles and dominates the strongly stochastic oscillations in small vesicles of submicrometer size. To this end, we derive reduced models, amenable to analytic treatments, and show that the accuracy of predictions, including the period of oscillations, is highly sensitive to the choice of the reduction scheme. In particular, we suggest an accurate two-variable model and show its equivalence to a three-variable model that admits an interpretation in terms of a chemical reaction network. The accurate description of a single pH oscillator appears crucial for rationalizing experiments and understanding communication of vesicles and synchronization of rhythms. T3 - ZIB-Report - 22-21 Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-88179 SN - 1438-0064 ER - TY - JOUR A1 - Gorgulla, Christoph A1 - Jayaraj, Abhilash A1 - Fackeldey, Konstantin A1 - Arthanari, Haribabu T1 - Emerging frontiers in virtual drug discovery: From quantum mechanical methods to deep learning approaches JF - Current Opinion in Chemical Biology N2 - Virtual screening-based approaches to discover initial hit and lead compounds have the potential to reduce both the cost and time of early drug discovery stages, as well as to find inhibitors for even challenging target sites such as protein–protein interfaces. Here in this review, we provide an overview of the progress that has been made in virtual screening methodology and technology on multiple fronts in recent years. The advent of ultra-large virtual screens, in which hundreds of millions to billions of compounds are screened, has proven to be a powerful approach to discover highly potent hit compounds. However, these developments are just the tip of the iceberg, with new technologies and methods emerging to propel the field forward. Examples include novel machine-learning approaches, which can reduce the computational costs of virtual screening dramatically, while progress in quantum-mechanical approaches can increase the accuracy of predictions of various small molecule properties. Y1 - 2022 U6 - https://doi.org/10.1016/j.cbpa.2022.102156 VL - 69 SP - 102156 EP - 102156-12 ER - TY - JOUR A1 - Sechi, Renata A1 - Fackeldey, Konstantin A1 - Chewle, Surahit A1 - Weber, Marcus T1 - SepFree NMF: A Toolbox for Analyzing the Kinetics of Sequential Spectroscopic Data JF - Algorithms N2 - This work addresses the problem of determining the number of components from sequential spectroscopic data analyzed by non-negative matrix factorization without separability assumption (SepFree NMF). These data are stored in a matrix M of dimension “measured times” versus “measured wavenumbers” and can be decomposed to obtain the spectral fingerprints of the states and their evolution over time. SepFree NMF assumes a memoryless (Markovian) process to underline the dynamics and decomposes M so that M=WH, with W representing the components’ fingerprints and H their kinetics. However, the rank of this decomposition (i.e., the number of physical states in the process) has to be guessed from pre-existing knowledge on the observed process. We propose a measure for determining the number of components with the computation of the minimal memory effect resulting from the decomposition; by quantifying how much the obtained factorization is deviating from the Markovian property, we are able to score factorizations of a different number of components. In this way, we estimate the number of different entities which contribute to the observed system, and we can extract kinetic information without knowing the characteristic spectra of the single components. This manuscript provides the mathematical background as well as an analysis of computer generated and experimental sequentially measured Raman spectra. Y1 - 2022 U6 - https://doi.org/10.3390/a15090297 VL - 15 IS - 9 SP - 297 ER - TY - CHAP A1 - Manley, Phillip A1 - Krüger, Jan A1 - Zschiedrich, Lin A1 - Hammerschmidt, Martin A1 - Bodermann, Bernd A1 - Köning, Rainer A1 - Schneider, Philipp-Immanuel T1 - Elementary, my dear Zernike: model order reduction for accelerating optical dimensional microscopy T2 - EPJ Web Conf. Y1 - 2022 U6 - https://doi.org/10.1051/epjconf/202226610010 VL - 266 SP - 10010 ER - TY - CHAP A1 - Krüger, Jan A1 - Manley, Phillip A1 - Zschiedrich, Lin A1 - Hammerschmidt, Martin A1 - Schneider, Philipp-Immanuel A1 - Köning, Rainer A1 - Bodermann, Bernd T1 - Untersuchung des Schwingungseinflusses auf gemessene Punktspreizfunktionen in der optischen Mikroskopie T2 - Proc. DGaO Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0287-2022-P005-6 SP - P5 ER - TY - CHAP A1 - Schneider, Philipp-Immanuel A1 - Zschiedrich, Lin A1 - Hammerschmidt, Martin A1 - Kuen, Lilli A1 - Sekulic, Ivan A1 - Kluge, Julien A1 - Leykauf, Bastian A1 - Krutzik, Markus A1 - Burger, Sven T1 - Boosting quantum optics experiments with Bayesian optimization T2 - Proc. SPIE Y1 - 2022 U6 - https://doi.org/10.1117/12.2632419 VL - 12227 SP - 122270F ER - TY - JOUR A1 - Rej, Sourav A1 - Santiago, Eva Yazmin A1 - Baturina, Olga A1 - Zhang, Yu A1 - Burger, Sven A1 - Kment, Stepan A1 - Govorov, Alexander O. A1 - Naldoni, Alberto T1 - Colloidal Titanium Nitride Nanobars for Broadband Inexpensive Plasmonics and Photochemistry from Visible to Mid-IR Wavelengths JF - Nano Energy Y1 - 2022 U6 - https://doi.org/10.1016/j.nanoen.2022.107989 VL - 104 SP - 107989 ER - TY - JOUR A1 - Ramos Leite, Tiago A1 - Zschiedrich, Lin A1 - Kizilkaya, Orhan A1 - McPeak, Kevin M. T1 - Resonant Plasmonic–Biomolecular Chiral Interactions in the Far-Ultraviolet: Enantiomeric Discrimination of sub-10 nm Amino Acid Films JF - Nano Lett. Y1 - 2022 UR - https://digitalcommons.lsu.edu/chem_engineering_pubs/34/ U6 - https://doi.org/10.1021/acs.nanolett.2c01724 VL - 22 SP - 7343 ER - TY - CHAP A1 - Dhanakoti, Siva Prasad Chakri A1 - Maddocks, John A1 - Weiser, Martin T1 - Navigation of Concentric Tube Continuum Robots using Optimal Control T2 - Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics N2 - Recently developed Concentric Tube Continuum Robots (CTCRs) are widely exploited in, for example in minimally invasive surgeries which involve navigating inside narrow body cavities close to sensitive regions. These CTCRs can be controlled by extending and rotating the tubes in order to reach a target point or perform some task. The robot must deviate as little as possible from this narrow space and avoid damaging neighbouring tissue. We consider \emph{open-loop} optimal control of CTCRs parameterized over pseudo-time, primarily aiming at minimizing the robot's working volume during its motion. External loads acting on the system like tip loads or contact with tissues are not considered here. We also discussed the inclusion of tip's orientation in the optimal framework to perform some tasks. We recall a quaternion-based formulation of the robot configuration, discuss discretization, develop optimization objectives addressing different criteria, and investigate their impact on robot path planning for several numerical examples. This optimal framework can be applied to any backbone based continuum robots. Y1 - 2022 U6 - https://doi.org/10.5220/0011271000003271 SP - 146 EP - 154 ER - TY - GEN A1 - Binkowski, Felix A1 - Betz, Fridtjof A1 - Hammerschmidt, Martin A1 - Schneider, Philipp-Immanuel A1 - Zschiedrich, Lin A1 - Burger, Sven T1 - Source code and simulation data for Computation of eigenfrequency sensitivities using Riesz projections for efficient optimization of nanophotonic resonators T2 - Zenodo Y1 - 2022 U6 - https://doi.org/10.5281/zenodo.6614951 ER - TY - GEN A1 - Plock, Matthias A1 - Andrle, Kas A1 - Burger, Sven A1 - Schneider, Philipp-Immanuel T1 - Research data and example scripts for the paper "Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction" T2 - Zenodo Y1 - 2022 U6 - https://doi.org/10.5281/zenodo.6359594 ER - TY - JOUR A1 - Erlekam, Franziska A1 - Zumbansen, Maximilian A1 - Weber, Marcus T1 - Parameter estimation on multivalent ITC data sets JF - Scientific Reports N2 - The Wiseman fitting can be used to extract binding parameters from ITC data sets, such as heat of binding, number of binding sites, and the overall dissociation rate. The classical Wiseman fitting assumes a direct binding process and neglects the possibility of intermediate binding steps. In principle, it only provides thermodynamic information and not the kinetics of the process. In this article we show that a concentration dependent dissociation constant could possibly stem from intermediate binding steps. The mathematical form of this dependency can be exploited with the aid of the Robust Perron Cluster Cluster Analysis method. Our proposed extension of the Wiseman fitting rationalizes the concentration dependency, and can probably also be used to determine the kinetic parameters of intermediate binding steps of a multivalent binding process. The novelty of this paper is to assume that the binding rate varies per titration step due to the change of the ligand concentration and to use this information in the Wiseman fitting. We do not claim to produce the most accurate values of the binding parameters, we rather present a novel method of how to approach multivalent bindings from a different angle. Y1 - 2022 U6 - https://doi.org/10.1038/s41598-022-17188-x VL - 12 SP - 13402 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 - Heindel, Tobias T1 - A Plug&Play Telecom-Wavelength Single-Photon Source for Quantum Key Distribution T2 - Quantum 2.0 Y1 - 2022 U6 - https://doi.org/10.1364/QUANTUM.2022.QTu4C.6 SP - QTu4C.6 ER - TY - THES A1 - Helfmann, Luzie T1 - Non-stationary Transition Path Theory with applications to tipping and agent-based models N2 - The interesting dynamical regimes in agent-based models (ABMs) of social dynamics are the transient dynamics leading to metastable or absorbing states, and the transition paths between metastable states possibly caused by external influences. In this thesis, we are particularly interested in the pathways of rare and critical transitions such as the tipping of the public opinion in a population or the forming of social movements. For a detailed quantitative analysis of these transition paths, we consider the agent-based models as Markov chains and employ Transition Path Theory. Since ABMs are usually not considered in stationarity and possibly even forced, we generalize Transition Path Theory to time-dependent dynamics, for example on finite-time intervals or with periodically varying transition probabilities. We also specifically consider the case of dynamics with absorbing states and show how the transitions prior to absorption can be studied. These generalizations can also be useful in other application domains such as for studying tipping in climate models or transitions in molecular models with external stimuli. Another obstacle when analysing the dynamics of agent-based models is the large number of agents resulting in a high-dimensional state space for the model. However, the emergent dynamics of the ABM usually has significantly fewer degrees of freedom and many symmetries enabling a model reduction. On the example of two stationary ABMs we demonstrate how a long model simulation can be employed to find a lower-dimensional parametrization of the state space using a manifold learning algorithm called Diffusion Maps. In the considered models, agents adapt their binary behaviour to the local neighbourhood. When the interaction network consists of several densely connected communities, the dynamics result in a largely coherent behaviour in each community. The low-dimensional structure of the state space is therefore a hypercube. The corners represent metastable states with coherent agent behaviour in each group and the edges correspond to transition paths where agents in a community change their behaviour through a chain reaction. Finally, we can apply Transition Path Theory to the effective dynamics in the reduced space to reveal, for example, the dominant transition paths or the agents that are most indicative of an impending tipping event. Y1 - 2022 UR - https://refubium.fu-berlin.de/handle/fub188/35660 U6 - https://doi.org/http://dx.doi.org/10.17169/refubium-35374 ER - TY - THES A1 - Niemann, Jan-Hendrik T1 - Learning Reduced Models for Large-Scale Agent-Based Systems N2 - Modeling social systems and studying their dynamical behavior plays an important role in many fields of research. Agent-based modeling provides a high degree of detail into artificial societies by describing the model from the perspective of the agents. The interactions of agents, often characterized by simple rules, lead to complex, time-evolving patterns. Their understanding is of great importance, e.g., for predicting and influencing epidemics. Analysis and simulation, however, often becomes prohibitively time-consuming when the number of agents or the time scale of interest is large. Therefore, this thesis is devoted to learn significantly reduced models of large-scale agent-based systems from simulation data. We show how data-driven methods based on transfer operators can be used to find reduced models represented by ordinary or stochastic differential equations that describe the dynamical behavior of larger groups or entire populations and thus enable the analysis and prediction of agent-based systems. To this end, we first present an extension of EDMD (extended dynamic mode decomposition) called gEDMD to approximate the Koopman generator from data. This method can be used to compute eigenfunctions, eigenvalues, and modes of the generator, as well as for system identification and model reduction of both deterministic and non-deterministic dynamical systems. Secondly, we analyze the long-term behavior of certain agent-based models and their pathwise approximations by stochastic differential equations for large numbers of agents using transfer operators. We show that, under certain conditions, the transfer operator approach connects the pathwise approximations on finite time scales with methods for describing the behavior on possibly exponentially long time scales. As a consequence, we can use the finite-time, pathwise approximations to characterize metastable behavior on long time scales using transfer operators. This can significantly reduce the computational cost. The third part addresses the data-driven model reduction since in many cases no analytical limit models are known or existent. We show how the Koopman operator theory can be used to infer the governing equations of agent-based systems directly from simulation data. Using benchmark problems, we demonstrate that for sufficiently large population sizes the data-driven models agree well with analytical limit equations and, moreover, that the reduced models allow predictions even in cases far from the limit or when no limit equations are known. Lastly, we demonstrate the potential of the presented approach. We present an ansatz for the multi-objective optimization of agent-based systems with the help of data-driven surrogate models based on the Koopman generator. In particular, when limit models are unknown or non-existent, this approach makes multi-objective optimization problems solvable that would otherwise be computationally infeasible due to very expensive objective functions. Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:188-refubium-35530-6 UR - https://refubium.fu-berlin.de/handle/fub188/35530 ER - TY - GEN A1 - Betz, Fridtjof A1 - Colom, Remi A1 - Burger, Sven A1 - Naydenov, Boris T1 - Scripts for quantifying the effect of diamond nano-pillars on the fluorescence of NV centers T2 - Zenodo Y1 - 2022 U6 - https://doi.org/10.5281/zenodo.6558815 ER - TY - JOUR A1 - Lopez-Fraguas, Eduardo A1 - Binkowski, Felix A1 - Burger, Sven A1 - Hagedorn, Sylvia A1 - Garcia-Camara, Braulio A1 - Vergaz, Ricardo A1 - Becker, Christiane A1 - Manley, Phillip T1 - Tripling the light extraction efficiency of a deep ultraviolet LED using a nanostructured p-contact JF - Sci. Rep. Y1 - 2022 U6 - https://doi.org/10.1038/s41598-022-15499-7 VL - 12 SP - 11480 ER - TY - JOUR A1 - Wang, Yujing A1 - Vannucci, Luca A1 - Burger, Sven A1 - Gregersen, Niels T1 - Near-unity efficiency in ridge waveguide-based, on-chip single-photon sources JF - Mater. Quantum Technol. Y1 - 2022 U6 - https://doi.org/10.1088/2633-4356/aca8e8 VL - 2 SP - 045004 ER - TY - JOUR A1 - Tillmann, Peter A1 - Jäger, Klaus A1 - Karsenti, Asher A1 - Kreinin, Lev A1 - Becker, Christiane T1 - Model-Chain Validation for Estimating the Energy Yield of Bifacial Perovskite/Silicon Tandem Solar Cells JF - Sol. RRL Y1 - 2022 U6 - https://doi.org/10.1002/solr.202200079 VL - 6 SP - 2200079 ER - TY - JOUR A1 - Benner, Peter A1 - Burger, Michael A1 - Göddeke, Dominik A1 - Görgen, Christiane A1 - Himpe, Christian A1 - Heiland, Jan A1 - Koprucki, Thomas A1 - Ohlberger, Mario A1 - Rave, Stephan A1 - Reidelbach, Marco A1 - Saak, Jens A1 - Schöbel, Anita A1 - Tabelow, Karsten A1 - Weber, Marcus T1 - Die mathematische Forschungsdateninitiative in der NFDI: MaRDI (Mathematical Research Data Initiative) JF - GAMM Rundbrief Y1 - 2022 IS - 1 SP - 40 EP - 43 ER - TY - JOUR A1 - Klus, Stefan A1 - Djurdjevac Conrad, Natasa T1 - Koopman-based spectral clustering of directed and time-evolving graphs JF - Journal of Nonlinear Science N2 - While spectral clustering algorithms for undirected graphs are well established and have been successfully applied to unsupervised machine learning problems ranging from image segmentation and genome sequencing to signal processing and social network analysis, clustering directed graphs remains notoriously difficult. Two of the main challenges are that the eigenvalues and eigenvectors of graph Laplacians associated with directed graphs are in general complex-valued and that there is no universally accepted definition of clusters in directed graphs. We first exploit relationships between the graph Laplacian and transfer operators and in particular between clusters in undirected graphs and metastable sets in stochastic dynamical systems and then use a generalization of the notion of metastability to derive clustering algorithms for directed and time-evolving graphs. The resulting clusters can be interpreted as coherent sets, which play an important role in the analysis of transport and mixing processes in fluid flows. Y1 - 2022 U6 - https://doi.org/10.1007/s00332-022-09863-0 VL - 33 ER - TY - GEN A1 - Colom, Remi A1 - Binkowski, Felix A1 - Betz, Fridtjof A1 - Kivshar, Yuri A1 - Burger, Sven T1 - Source code and simulation results for nanoantennas supporting an enhanced Purcell factor due to interfering resonances T2 - Zenodo Y1 - 2022 U6 - https://doi.org/10.5281/zenodo.6565850 ER - TY - JOUR A1 - Avalos-Ovando, Oscar A1 - Santiago, Eva Yazmin A1 - Movsesyan, Artur A1 - Kong, Xiang-Tian A1 - Yu, Peng A1 - Besteiro, Lucas V. A1 - Khosravi Khorashad, Larousse A1 - Okamoto, Hiromi A1 - Slocik, Joseph M. A1 - Correa-Duarte, Miguel A1 - Comesana-Hermo, Miguel A1 - Liedl, Tim A1 - Markovich, Gil A1 - Burger, Sven A1 - Govorov, Alexander O. T1 - Chiral Bioinspired Plasmonics: A Paradigm Shift for Optical Activity and Photochemistry JF - ACS Photon. Y1 - 2022 U6 - https://doi.org/10.1021/acsphotonics.2c00445 VL - 9 SP - 2219 ER - TY - CHAP A1 - Jäger, Klaus A1 - Sutter, Johannes A1 - Tockhorn, Philipp A1 - Hammerschmidt, Martin A1 - Schneider, Philipp-Immanuel A1 - Albrecht, Steve A1 - Becker, Christiane T1 - Improving perovskite/silicon tandem solar cells with optical simulations T2 - Proc. SPIE Y1 - 2022 U6 - https://doi.org/10.1117/12.2621255 VL - PC12150 SP - PC1215001 ER - TY - JOUR A1 - Schneider, Philipp-Immanuel A1 - Manley, Phillip A1 - Krüger, Jan A1 - Zschiedrich, Lin A1 - Köning, Rainer A1 - Bodermann, Bernd A1 - Burger, Sven T1 - Reconstructing phase aberrations for high-precision dimensional microscopy JF - Proc. SPIE Y1 - 2022 U6 - https://doi.org/10.1117/12.2620762 VL - 12137 SP - 121370I ER -