Nonlinear stochastic modeling with Langevin regression

TL;DR

This paper introduces a framework for learning interpretable nonlinear stochastic models from data, addressing challenges like non-Gaussian noise and non-Markovian effects in complex systems such as turbulence.

Contribution

It develops a method combining forward and adjoint Fokker-Planck equations with sparsity techniques to identify stochastic nonlinear dynamics from experimental data.

Findings

Successfully recovers nonlinear Langevin equations with colored noise.

Approximates bifurcation normal forms for complex dynamics.

Models turbulent wake behavior with state-dependent noise.

Abstract

Many physical systems characterized by nonlinear multiscale interactions can be effectively modeled by treating unresolved degrees of freedom as random fluctuations. However, even when the microscopic governing equations and qualitative macroscopic behavior are known, it is often difficult to derive a stochastic model that is consistent with observations. This is especially true for systems such as turbulence where the perturbations do not behave like Gaussian white noise, introducing non-Markovian behavior to the dynamics. We address these challenges with a framework for identifying interpretable stochastic nonlinear dynamics from experimental data, using both forward and adjoint Fokker-Planck equations to enforce statistical consistency. If the form of the Langevin equation is unknown, a simple sparsifying procedure can provide an appropriate functional form. We demonstrate that this method can effectively learn stochastic models in two artificial examples: recovering a nonlinear Langevin equation forced by colored noise and approximating the second-order dynamics of a particle in a double-well potential with the corresponding first-order bifurcation normal form. Finally, we apply the proposed method to experimental measurements of a turbulent bluff body wake and show that the statistical behavior of the center of pressure can be described by the dynamics of the corresponding laminar flow driven by nonlinear state-dependent noise.