Optimal Data-Driven Estimation of Generalized Markov State Models for Non-Equilibrium Dynamics
AbstractThere are multiple ways in which a stochastic system can be out of statistical equilibrium. It might be subject to time-varying forcing; or be in a transient phase on its way towards equilibrium; it might even be in equilibrium without us noticing it, due to insufficient observations; and it even might be a system failing to admit an equilibrium distribution at all. We review some of the approaches that model the effective statistical behavior of equilibrium and non-equilibrium dynamical systems, and show that both cases can be considered under the unified framework of optimal low-rank approximation of so-called transfer operators. Particular attention is given to the connection between these methods, Markov state models, and the concept of metastability, further to the estimation of such reduced order models from finite simulation data. All these topics bear an important role in, e.g., molecular dynamics, where Markov state models are often and successfully utilized, and which is the main motivating application in this paper. We illustrate our considerations by numerical examples. View Full-Text
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Koltai, P.; Wu, H.; Noé, F.; Schütte, C. Optimal Data-Driven Estimation of Generalized Markov State Models for Non-Equilibrium Dynamics. Computation 2018, 6, 22.
Koltai P, Wu H, Noé F, Schütte C. Optimal Data-Driven Estimation of Generalized Markov State Models for Non-Equilibrium Dynamics. Computation. 2018; 6(1):22.Chicago/Turabian Style
Koltai, Péter; Wu, Hao; Noé, Frank; Schütte, Christof. 2018. "Optimal Data-Driven Estimation of Generalized Markov State Models for Non-Equilibrium Dynamics." Computation 6, no. 1: 22.
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