Optimal data-driven estimation of generalized Markov state models for non-equilibrium dynamics

TL;DR

This paper reviews methods for modeling both equilibrium and non-equilibrium stochastic systems using optimal low-rank approximations of transfer operators, emphasizing their connection to Markov state models and metastability.

Contribution

It unifies the modeling of equilibrium and non-equilibrium systems under a common framework of optimal transfer operator approximation, with focus on data-driven estimation.

Findings

Unified framework for equilibrium and non-equilibrium modeling

Connection between transfer operators and Markov state models

Numerical examples demonstrating the approach

Abstract

There 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. We illustrate our considerations by numerical examples.