Data-driven Discovery of Invariant Measures

TL;DR

This paper introduces a data-driven, optimization-based approach to discover invariant measures of dynamical systems directly from data, applicable to both deterministic and stochastic systems without requiring explicit models.

Contribution

It presents a novel method that targets specific invariant measures from data, with proven convergence and superior performance over previous model-based approaches.

Findings

Successfully applied to logistic map and stochastic double-well system

Accurately approximates physical measures of chaotic attractors

Outperforms previous methods based on model identification

Abstract

Invariant measures encode the long-time behaviour of a dynamical system. In this work, we propose an optimization-based method to discover invariant measures directly from data gathered from a system. Our method does not require an explicit model for the dynamics and allows one to target specific invariant measures, such as physical and ergodic measures. Moreover, it applies to both deterministic and stochastic dynamics in either continuous or discrete time. We provide convergence results and illustrate the performance of our method on data from the logistic map and a stochastic double-well system, for which invariant measures can be found by other means. We then use our method to approximate the physical measure of the chaotic attractor of the R\"ossler system, and we extract unstable periodic orbits embedded in this attractor by identifying discrete-time periodic points of a suitably defined Poincar\'e map. This final example is truly data-driven and shows that our method can significantly outperform previous approaches based on model identification.