TY - JOUR A1 - Wang, Han A1 - Schütte, Christof A1 - Zhang, P. T1 - Error Estimate of Short-Range Force Calculation in the Inhomogeneous Molecular Systems JF - Phys. Rev. E Y1 - 2012 UR - http://publications.imp.fu-berlin.de/1125/ U6 - https://doi.org/10.1103/PhysRevE.86.026704 VL - 86 IS - 02 SP - 026704 ER - TY - JOUR A1 - Wang, Han A1 - Zhang, P. A1 - Schütte, Christof T1 - On the Numerical Accuracy of Ewald, Smooth Particle Mesh Ewald, and Staggered Mesh Ewald Methods for Correlated Molecular Systems JF - J. Chem. Theo. Comp. Y1 - 2012 UR - http://publications.imp.fu-berlin.de/1181/ U6 - https://doi.org/10.1021/ct300343y VL - 8 IS - 9 SP - 3243 EP - 3256 ER - TY - JOUR A1 - Weber, Marcus A1 - Fackeldey, Konstantin A1 - Schütte, Christof T1 - Set-Free Markov State Model Building JF - Journal of Chemical Physics Y1 - 2017 U6 - https://doi.org/10.1063/1.4978501 VL - 146 IS - 12 ER - TY - GEN A1 - Weber, Marcus A1 - Fackeldey, Konstantin A1 - Schütte, Christof T1 - Set-free Markov State Building N2 - Molecular dynamics (MD) simulations face challenging problems since the timescales of interest often are much longer than what is possible to simulate and even if sufficiently long simulation are possible the complex nature of the resulting simulation data makes interpretation difficult. Markov State Models (MSMs) help to overcome these problems by making experimentally relevant timescales accessible via coarse grained representations that also allows for convenient interpretation. However, standard set-based MSMs exhibit some caveats limiting their approximation quality and statistical significance. One of the main caveats results from the fact that typical MD trajectories repeatedly re-cross the boundary between the sets used to build the MSM which causes statistical bias in estimating the transition probabilities between these sets. In this article, we present a set-free approach to MSM building utilizing smooth overlapping ansatz functions instead of sets and an adaptive refinement approach. This kind of meshless discretization helps to overcome the recrossing problem and yields an adaptive refinement procedure that allows to improve the quality of the model while exploring state space and inserting new ansatz functions into the MSM. T3 - ZIB-Report - 17-10 Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-62167 SN - 1438-0064 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 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 - 2024 ER - TY - JOUR A1 - Weiss, Maximilian A1 - Paulus, Florian A1 - Steinhilber, D. A1 - Nikitin, Anatoly A1 - Haag, Rainer A1 - Schütte, Christof T1 - Estimating Kinetic Parameters for the Spontaneous Polymerization of Glycidol at Elevated Temperatures JF - MACROMOLECULAR THEORY and SIMULATIONS Y1 - 2012 UR - http://publications.imp.fu-berlin.de/1142/ U6 - https://doi.org/10.1002/mats.201200003 VL - 21 IS - 7 SP - 470 EP - 481 ER - TY - GEN A1 - Willenbockel, Christian Tobias A1 - Schütte, Christof T1 - Variational Bayesian Inference and Model Selection for the Stochastic Block Model with Irrelevant Vertices N2 - Real World networks often exhibit a significant number of vertices which are sparsely and irregularly connected to other vertices in the network. For clustering theses networks with a model based algorithm, we propose the Stochastic Block Model with Irrelevant Vertices (SBMIV) for weighted net- works. We propose an original Variational Bayesian Expectation Maximiza- tion inference algorithm for the SBMIV which is an advanced version of our Blockloading algorithm for the Stochastic Block Model. We introduce a model selection criterion for the number of clusters of the SBMIV which is based on the lower variational bound of the model likelihood. We propose a fully Bayesian inference process, based on plausible informative priors, which is independent of other algorithms for preprocessing start values for the cluster assignment of vertices. Our inference methods allow for a multi level identification of irrelevant vertices which are hard to cluster reliably ac- cording to the SBM. We demonstrate that our methods improve on the normal Stochastic Block model by applying it to to Earthquake Networks which are an example of networks with a large number of sparsely and irregularly con- nected vertices. T3 - ZIB-Report - 16-01 KW - Clustering KW - Variational Bayes EM KW - Model Selection, KW - Stochastic Block Model KW - Networks KW - unsupervised classification KW - Noise Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-57003 SN - 1438-0064 ER - TY - GEN A1 - Willenbockel, Christian Tobias A1 - Schütte, Christof T1 - A Variational Bayesian Algorithm for Clustering of Large and Complex Networks N2 - We propose the Blockloading algorithm for the clustering of large and complex graphs with tens of thousands of vertices according to a Stochastic Block Model (SBM). Blockloading is based on generalized Variational Bayesian EM (VBEM) schemes and works for weighted and unweighted graphs. Existing Variational (Bayesian) EM methods have to consider each possible number of clusters sepa- rately to determine the optimal number of clusters and are prone to converge to local optima making multiple restarts necessary. These factors impose a severe restriction on the size and complexity of graphs these methods can handle. In con- trast, the Blockloading algorithm restricts restarts to subnetworks in a way that provides error correction of an existing cluster assignment. The number of clusters need not be specified in advance because Blockloading will return it as a result. We show that Blockloading outperforms all other variational methods regarding reliability of the results and computational efficiency. T3 - ZIB-Report - 15-25 KW - Clustering KW - Variational Bayes EM KW - Model Selection, KW - Stochastic Block Model KW - Networks KW - unsupervised classification Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-54588 SN - 1438-0064 ER - TY - JOUR A1 - Winkelmann, Stefanie A1 - Schütte, Christof T1 - Hybrid models for chemical reaction networks: Multiscale theory and application to gene regulatory systems JF - The Journal of Chemical Physics N2 - Well-mixed stochastic chemical kinetics are properly modeled by the chemical master equation (CME) and associated Markov jump processes in molecule number space. If the reactants are present in large amounts, however, corresponding simulations of the stochastic dynamics become computationally expensive and model reductions are demanded. The classical model reduction approach uniformly rescales the overall dynamics to obtain deterministic systems characterized by ordinary differential equations, the well-known mass action reaction rate equations. For systems with multiple scales, there exist hybrid approaches that keep parts of the system discrete while another part is approximated either using Langevin dynamics or deterministically. This paper aims at giving a coherent overview of the different hybrid approaches, focusing on their basic concepts and the relation between them. We derive a novel general description of such hybrid models that allows expressing various forms by one type of equation. We also check in how far the approaches apply to model extensions of the CME for dynamics which do not comply with the central well-mixed condition and require some spatial resolution. A simple but meaningful gene expression system with negative self-regulation is analysed to illustrate the different approximation qualities of some of the hybrid approaches discussed. Especially, we reveal the cause of error in the case of small volume approximations. Y1 - 2017 U6 - https://doi.org/10.1063/1.4986560 VL - 147 IS - 11 SP - 114115-1 EP - 114115-18 ER - TY - JOUR A1 - Winkelmann, Stefanie A1 - Schütte, Christof T1 - The Spatiotemporal Master Equation: Approximation of Reaction-Diffusion Dynamics via Markov State Modeling JF - Journal of Chemical Physics N2 - Accurate modeling and numerical simulation of reaction kinetics is a topic of steady interest.We consider the spatiotemporal chemical master equation (ST-CME) as a model for stochastic reaction-diffusion systems that exhibit properties of metastability. The space of motion is decomposed into metastable compartments and diffusive motion is approximated by jumps between these compartments. Treating these jumps as first-order reactions, simulation of the resulting stochastic system is possible by the Gillespie method. We present the theory of Markov state models (MSM) as a theoretical foundation of this intuitive approach. By means of Markov state modeling, both the number and shape of compartments and the transition rates between them can be determined. We consider the ST-CME for two reaction-diffusion systems and compare it to more detailed models. Moreover, a rigorous formal justification of the ST-CME by Galerkin projection methods is presented. Y1 - 2016 U6 - https://doi.org/10.1063/1.4971163 VL - 145 IS - 21 ER - TY - GEN A1 - Winkelmann, Stefanie A1 - Schütte, Christof T1 - Hybrid Models for Chemical Reaction Networks: Multiscale Theory and Application to Gene Regulatory Systems N2 - Well-mixed stochastic chemical kinetics are properly modelled by the chemical master equation (CME) and associated Markov jump processes in molecule number space. If the reactants are present in large amounts, however, corresponding simulations of the stochastic dynamics become computationally expensive and model reductions are demanded. The classical model reduction approach uniformly rescales the overall dynamics to obtain deterministic systems characterized by ordinary differential equations, the well-known mass action reaction rate equations. For systems with multiple scales there exist hybrid approaches that keep parts of the system discrete while another part is approximated either using Langevin dynamics or deterministically. This paper aims at giving a coherent overview of the different hybrid approaches, focusing on their basic concepts and the relation between them. We derive a novel general description of such hybrid models that allows to express various forms by one type of equation. We also check in how far the approaches apply to model extensions of the CME for dynamics which do not comply with the central well-mixed condition and require some spatial resolution. A simple but meaningful gene expression system with negative self-regulation is analysed to illustrate the different approximation qualities of some of the hybrid approaches discussed. T3 - ZIB-Report - 17-29 Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-64264 ER - TY - BOOK A1 - Winkelmann, Stefanie A1 - Schütte, Christof T1 - Stochastic Dynamics in Computational Biology T3 - Frontiers in Applied Dynamical Systems: Reviews and Tutorials Y1 - 2020 SN - 978-3-030-62386-9 U6 - https://doi.org/10.1007/978-3-030-62387-6 VL - 8 PB - Springer International Publishing ER - TY - GEN A1 - Winkelmann, Stefanie A1 - Schütte, Christof T1 - The spatiotemporal master equation: approximation of reaction-diffusion dynamics via Markov state modeling N2 - Accurate modeling and numerical simulation of reaction kinetics is a topic of steady interest. We consider the spatiotemporal chemical master equation (ST-CME) as a model for stochastic reaction-diffusion systems that exhibit properties of metastability. The space of motion is decomposed into metastable compartments and diffusive motion is approximated by jumps between these compartments. Treating these jumps as first-order reactions, simulation of the resulting stochastic system is possible by the Gillespie method. We present the theory of Markov state models (MSM) as a theoretical foundation of this intuitive approach. By means of Markov state modeling, both the number and shape of compartments and the transition rates between them can be determined. We consider the ST-CME for two reaction-diffusion systems and compare it to more detailed models. Moreover, a rigorous formal justification of the ST-CME by Galerkin projection methods is presented. T3 - ZIB-Report - 16-60 KW - reaction-diffusion KW - stochastic chemical kinetics KW - chemical master equation Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-60999 SN - 1438-0064 ER - TY - JOUR A1 - Winkelmann, Stefanie A1 - Schütte, Christof A1 - von Kleist, Max T1 - Markov Control with Rare State Observation JF - International Journal of Biomathematics and Biostatistics Y1 - 2012 UR - http://publications.mi.fu-berlin.de/1177/ ER - TY - GEN A1 - Winkelmann, Stefanie A1 - Schütte, Christof A1 - von Kleist, Max T1 - Markov Control Processes with Rare State Observation: Theory and Application to Treatment Scheduling in HIV-1 N2 - Markov Decision Processes (MDP) or Partially Observable MDPs (POMDP) are used for modelling situations in which the evolution of a process is partly random and partly controllable. These MDP theories allow for computing the optimal control policy for processes that can continuously or frequently be observed, even if only partially. However, they cannot be applied if state observation is very costly and therefore rare (in time). We present a novel MDP theory for rare, costly observations and derive the corresponding Bellman equation. In the new theory, state information can be derived for a particular cost after certain, rather long time intervals. The resulting information costs enter into the total cost and thus into the optimization criterion. This approach applies to many real world problems, particularly in the medical context, where the medical condition is examined rather rarely because examination costs are high. At the same time, the approach allows for efficient numerical realization. We demonstrate the usefulness of the novel theory by determining, from the national economic perspective, optimal therapeutic policies for the treatment of the human immunodefficiency virus (HIV) in resource-rich and resource-poor settings. Based on the developed theory and models, we discover that available drugs may not be utilized efficiently in resource-poor settings due to exorbitant diagnostic costs. T3 - ZIB-Report - 13-34 KW - information costs KW - hidden state KW - bellmann equation KW - optimal therapeutic policies KW - diagnostic frequency KW - resource-poor KW - resource-rich Y1 - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-41955 SN - 1438-0064 ER - TY - JOUR A1 - Winkelmann, Stefanie A1 - Schütte, Christof A1 - von Kleist, Max T1 - Markov Control Processes with Rare State Observation: Sensitivity Analysis with Respect to Optimal Treatment Strategies against HIV-1 JF - International Journal of Biomathematics and Biostatistics N2 - We present the theory of “Markov decision processes (MDP) with rare state observation” and apply it to optimal treatment scheduling and diagnostic testing to mitigate HIV-1 drug resistance development in resource-poor countries. The developed theory assumes that the state of the process is hidden and can only be determined by making an examination. Each examination produces costs which enter into the considered cost functional so that the resulting optimization problem includes finding optimal examination times. This is a realistic ansatz: In many real world applications, like HIV-1 treatment scheduling, the information about the disease evolution involves substantial costs, such that examination and control are intimately connected. However, a perfect compliance with the optimal strategy can rarely be achieved. This may be particularly true for HIV-1 resistance testing in resource-constrained countries. In the present work, we therefore analyze the sensitivity of the costs with respect to deviations from the optimal examination times both analytically and for the considered application. We discover continuity in the cost-functional with respect to the examination times. For the HIV-application, moreover, sensitivity towards small deviations from the optimal examination rule depends on the disease state. Furthermore, we compare the optimal rare-control strategy to (i) constant control strategies (one action for the remaining time) and to (ii) the permanent control of the original, fully observed MDP. This comparison is done in terms of expected costs and in terms of life-prolongation. The proposed rare-control strategy offers a clear benefit over a constant control, stressing the usefulness of medical testing and informed decision making. This indicates that lower-priced medical tests could improve HIV treatment in resource-constrained settings and warrants further investigation. Y1 - 2013 VL - 2 IS - 1 ER - TY - JOUR A1 - Winkelmann, Stefanie A1 - Schütte, Christof A1 - von Kleist, Max T1 - Markov Control Processes with Rare State Observation: Theory and Application to Treatment Scheduling in HIV-1 JF - Communications in Mathematical Sciences N2 - Markov Decision Processes (MDP) or Partially Observable MDPs (POMDP) are used for modelling situations in which the evolution of a process is partly random and partly controllable. These MDP theories allow for computing the optimal control policy for processes that can continuously or frequently be observed, even if only partially. However, they cannot be applied if state observation is very costly and therefore rare (in time). We present a novel MDP theory for rare, costly observations and derive the corresponding Bellman equation. In the new theory, state information can be derived for a particular cost after certain, rather long time intervals. The resulting information costs enter into the total cost and thus into the optimization criterion. This approach applies to many real world problems, particularly in the medical context, where the medical condition is examined rather rarely because examination costs are high. At the same time, the approach allows for efficient numerical realization. We demonstrate the usefulness of the novel theory by determining, from the national economic perspective, optimal therapeutic policies for the treatment of the human immunodeficiency virus (HIV) in resource-rich and resource-poor settings. Based on the developed theory and models, we discover that available drugs may not be utilized efficiently in resource-poor settings due to exorbitant diagnostic costs. Y1 - 2014 U6 - https://doi.org/10.4310/CMS.2014.v12.n5.a4 VL - 12 IS - 5 SP - 859 EP - 877 ER - TY - JOUR A1 - Winkelmann, Stefanie A1 - Schütte, Christof A1 - von Kleist, Max T1 - Markov Control Processes with Rare State Observation JF - Communications in Mathematical Sciences Y1 - 2012 UR - http://publications.imp.fu-berlin.de/1176/ VL - 12 IS - 859 ER - TY - JOUR A1 - Winkelmann, Stefanie A1 - Zonker, Johannes A1 - Schütte, Christof A1 - Djurdjevac Conrad, Natasa T1 - Mathematical modeling of spatio-temporal population dynamics and application to epidemic spreading JF - Mathematical Biosciences N2 - Agent based models (ABMs) are a useful tool for modeling spatio-temporal population dynamics, where many details can be included in the model description. Their computational cost though is very high and for stochastic ABMs a lot of individual simulations are required to sample quantities of interest. Especially, large numbers of agents render the sampling infeasible. Model reduction to a metapopulation model leads to a significant gain in computational efficiency, while preserving important dynamical properties. Based on a precise mathematical description of spatio-temporal ABMs, we present two different metapopulation approaches (stochastic and piecewise deterministic) and discuss the approximation steps between the different models within this framework. Especially, we show how the stochastic metapopulation model results from a Galerkin projection of the underlying ABM onto a finite-dimensional ansatz space. Finally, we utilize our modeling framework to provide a conceptual model for the spreading of COVID-19 that can be scaled to real-world scenarios. Y1 - 2021 U6 - https://doi.org/10.1016/j.mbs.2021.108619 VL - 336 PB - Elsevier ER - TY - JOUR A1 - Wulkow, Hanna A1 - Conrad, Tim A1 - Djurdjevac Conrad, Natasa A1 - Müller, Sebastian A. A1 - Nagel, Kai A1 - Schütte, Christof T1 - Prediction of Covid-19 spreading and optimal coordination of counter-measures: From microscopic to macroscopic models to Pareto fronts JF - PLOS One Y1 - 2021 U6 - https://doi.org/10.1371/journal.pone.0249676 VL - 16 IS - 4 PB - Public Library of Science ER - TY - JOUR A1 - Wulkow, Niklas A1 - Koltai, Péter A1 - Schütte, Christof T1 - Memory-Based Reduced Modelling and Data-Based Estimation of Opinion Spreading JF - Journal of Nonlinear Science N2 - We investigate opinion dynamics based on an agent-based model and are interested in predicting the evolution of the percentages of the entire agent population that share an opinion. Since these opinion percentages can be seen as an aggregated observation of the full system state, the individual opinions of each agent, we view this in the framework of the Mori–Zwanzig projection formalism. More specifically, we show how to estimate a nonlinear autoregressive model (NAR) with memory from data given by a time series of opinion percentages, and discuss its prediction capacities for various specific topologies of the agent interaction network. We demonstrate that the inclusion of memory terms significantly improves the prediction quality on examples with different network topologies. Y1 - 2021 U6 - https://doi.org/10.1007/s00332-020-09673-2 VL - 31 ER - TY - JOUR A1 - Wulkow, Niklas A1 - Koltai, Péter A1 - Sunkara, Vikram A1 - Schütte, Christof T1 - Data-driven modelling of nonlinear dynamics by barycentric coordinates and memory JF - J. Stat. Phys. N2 - We present a numerical method to model dynamical systems from data. We use the recently introduced method Scalable Probabilistic Approximation (SPA) to project points from a Euclidean space to convex polytopes and represent these projected states of a system in new, lower-dimensional coordinates denoting their position in the polytope. We then introduce a specific nonlinear transformation to construct a model of the dynamics in the polytope and to transform back into the original state space. To overcome the potential loss of information from the projection to a lower-dimensional polytope, we use memory in the sense of the delay-embedding theorem of Takens. By construction, our method produces stable models. We illustrate the capacity of the method to reproduce even chaotic dynamics and attractors with multiple connected components on various examples. Y1 - 2021 ER - TY - JOUR A1 - Wulkow, Niklas A1 - Telgmann, Regina A1 - Hungenberg, Klaus-Dieter A1 - Schütte, Christof A1 - Wulkow, Michael T1 - Deterministic and Stochastic Parameter Estimation for Polymer Reaction Kinetics I: Theory and Simple Examples JF - Macromolecular Theory and Simulations N2 - Two different approaches to parameter estimation (PE) in the context of polymerization are introduced, refined, combined, and applied. The first is classical PE where one is interested in finding parameters which minimize the distance between the output of a chemical model and experimental data. The second is Bayesian PE allowing for quantifying parameter uncertainty caused by experimental measurement error and model imperfection. Based on detailed descriptions of motivation, theoretical background, and methodological aspects for both approaches, their relation are outlined. The main aim of this article is to show how the two approaches complement each other and can be used together to generate strong information gain regarding the model and its parameters. Both approaches and their interplay in application to polymerization reaction systems are illustrated. This is the first part in a two-article series on parameter estimation for polymer reaction kinetics with a focus on theory and methodology while in the second part a more complex example will be considered. Y1 - 2021 U6 - https://doi.org/10.1002/mats.202100017 VL - 30 ER - TY - GEN A1 - Zhang, Wei A1 - Hartmann, Carsten A1 - Schütte, Christof T1 - Effective Dynamics Along Given Reaction Coordinates, and Reaction Rate Theory N2 - In molecular dynamics and related fields one considers dynamical descriptions of complex systems in full (atomic) detail. In order to reduce the overwhelming complexity of realistic systems (high dimension, large timescale spread, limited computational resources) the projection of the full dynamics onto some reaction coordinates is examined in order to extract statistical information like free energies or reaction rates. In this context, the effective dynamics that is induced by the full dynamics on the reaction coordinate space has attracted considerable attention in the literature. In this article, we contribute to this discussion: We first show that if we start with an ergodic diffusion processes whose invariant measure is unique then these properties are inherited by the effective dynamics. Then, we give equations for the effective dynamics, discuss whether the dominant timescales and reaction rates inferred from the effective dynamics are accurate approximations of such quantities for the full dynamics, and compare our findings to results from approaches like Zwanzig-Mori, averaging, or homogenization. Finally, by discussing the algorithmic realization of the effective dynamics, we demonstrate that recent algorithmic techniques like the ”equation-free” approach and the ”heterogeneous multiscale method” can be seen as special cases of our approach. T3 - ZIB-Report - 16-35 KW - Ergodic diffusion KW - reaction coordinate KW - effective dynamics KW - model reduction KW - equation-free KW - heterogeneous multiscale method Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-59706 SN - 1438-0064 ER - TY - JOUR A1 - Zhang, Wei A1 - Hartmann, Carsten A1 - Schütte, Christof T1 - Effective dynamics along given reaction coordinates, and reaction rate theory JF - Faraday Discussions Y1 - 2016 U6 - https://doi.org/10.1039/C6FD00147E IS - 195 SP - 365 EP - 394 ER - TY - JOUR A1 - Zhang, Wei A1 - Klus, Stefan A1 - Conrad, Tim A1 - Schütte, Christof T1 - Learning chemical reaction networks from trajectory data JF - SIAM Journal on Applied Dynamical Systems (SIADS) N2 - We develop a data-driven method to learn chemical reaction networks from trajectory data. Modeling the reaction system as a continuous-time Markov chain and assuming the system is fully observed,our method learns the propensity functions of the system with predetermined basis functions by maximizing the likelihood function of the trajectory data under l^1 sparse regularization. We demonstrate our method with numerical examples using synthetic data and carry out an asymptotic analysis of the proposed learning procedure in the infinite-data limit. Y1 - 2019 U6 - https://doi.org/10.1137/19M1265880 VL - 18 IS - 4 SP - 2000 EP - 2046 ER - TY - JOUR A1 - Zhang, Wei A1 - Li, Tiejun A1 - Schütte, Christof T1 - Solving eigenvalue PDEs of metastable diffusion processes using artificial neural networks JF - Journal of Computational Physics N2 - In this paper, we consider the eigenvalue PDE problem of the infinitesimal generators of metastable diffusion processes. We propose a numerical algorithm based on training artificial neural networks for solving the leading eigenvalues and eigenfunctions of such high-dimensional eigenvalue problem. The algorithm is useful in understanding the dynamical behaviors of metastable processes on large timescales. We demonstrate the capability of our algorithm on a high-dimensional model problem, and on the simple molecular system alanine dipeptide. Y1 - 2021 U6 - https://doi.org/10.1016/j.jcp.2022.111377 VL - 465 ER - TY - JOUR A1 - Zhang, Wei A1 - Schütte, Christof T1 - Reliable approximation of long relaxation timescales in molecular dynamics JF - Entropy Y1 - 2017 U6 - https://doi.org/10.3390/e19070367 VL - 19 IS - 7 ER - TY - GEN A1 - Zhang, Wei A1 - Schütte, Christof T1 - Reliable approximation of long relaxation timescales in molecular dynamics N2 - Many interesting rare events in molecular systems like ligand association, protein folding or con- formational changes happen on timescales that often are not accessible by direct numerical simulation. Therefore rare event approximation approaches like interface sampling, Markov state model building or advanced reaction coordinate based free energy estimation have attracted huge attention recently. In this article we analyze the reliability of such approaches: How precise is an estimate of long relaxation timescales of molecular systems resulting from various forms of rare event approximation methods? Our results give a theoretical answer to this question by relating it with the transfer operator approach to molecular dynamics. By doing so they also allow for understanding deep connections between the different approaches. T3 - ZIB-Report - 17-19 Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-63718 SN - 1438-0064 ER - TY - JOUR A1 - Zhang, Wei A1 - Schütte, Christof T1 - Understanding recent deep-learning techniques for identifying collective variables of molecular dynamics BT - Special Issue: 93rd Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM) JF - Proceedings in Applied Mathematics and Mechanics N2 - High-dimensional metastable molecular dynamics (MD) can often be characterised by a few features of the system, that is, collective variables (CVs). Thanks to the rapid advance in the area of machine learning and deep learning, various deep learning-based CV identification techniques have been developed in recent years, allowing accurate modelling and efficient simulation of complex molecular systems. In this paper, we look at two different categories of deep learning-based approaches for finding CVs, either by computing leading eigenfunctions of transfer operator associated to the underlying dynamics, or by learning an autoencoder via minimisation of reconstruction error. We present a concise overview of the mathematics behind these two approaches and conduct a comparative numerical study of these two approaches on illustrative examples. Y1 - 2023 U6 - https://doi.org/10.1002/pamm.202300189 VL - 23 IS - 4 ER - TY - JOUR A1 - Zhang, Wei A1 - Schütte, Christof T1 - On finding optimal collective variables for complex systems by minimizing the deviation between effective and full dynamics N2 - This paper is concerned with collective variables, or reaction coordinates, that map a discrete-in-time Markov process X_n in R^d to a (much) smaller dimension k≪d. We define the effective dynamics under a given collective variable map ξ as the best Markovian representation of X_n under ξ. The novelty of the paper is that it gives strict criteria for selecting optimal collective variables via the properties of the effective dynamics. In particular, we show that the transition density of the effective dynamics of the optimal collective variable solves a relative entropy minimization problem from certain family of densities to the transition density of X_n. We also show that many transfer operator-based data-driven numerical approaches essentially learn quantities of the effective dynamics. Furthermore, we obtain various error estimates for the effective dynamics in approximating dominant timescales / eigenvalues and transition rates of the original process X_n and how optimal collective variables minimize these errors. Our results contribute to the development of theoretical tools for the understanding of complex dynamical systems, e.g. molecular kinetics, on large timescales. These results shed light on the relations among existing data-driven numerical approaches for identifying good collective variables, and they also motivate the development of new methods. Y1 - 2024 ER - TY - GEN A1 - Zhang, Wei A1 - Wang, Han A1 - Hartmann, Carsten A1 - Weber, Marcus A1 - Schütte, Christof T1 - Applications of the cross-entropy method to importance sampling and optimal control of diffusions N2 - We study the cross-entropy method for diffusions. One of the results is a versatile cross-entropy algorithm that can be used to design efficient importance sampling strategies for rare events or to solve optimal control problems. The approach is based on the minimization of a suitable cross-entropy functional, with a parametric family of exponentially tilted probability distributions. We illustrate the new algorithm with several numerical examples and discuss algorithmic issues and possible extensions of the method. T3 - ZIB-Report - 14-10 KW - important sampling KW - optimal control KW - cross-entropy method KW - rare events KW - change of measure Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-49720 SN - 1438-0064 ER - TY - JOUR A1 - Zhang, Wei A1 - Wang, Han A1 - Hartmann, Carsten A1 - Weber, Marcus A1 - Schütte, Christof T1 - Applications of the cross-entropy method to importance sampling and optimal control of diffusions JF - Siam Journal on Scientific Computing Y1 - 2014 U6 - https://doi.org/10.1137/14096493X VL - 36 IS - 6 SP - A2654 EP - A2672 ER -