Improvement of system identification of stochastic systems via Koopman generator and locally weighted expectation

TL;DR

This paper enhances the system identification of stochastic systems by improving the Koopman generator approach with clustering techniques to better handle noise and nonlinearities, demonstrated on a double-well potential system.

Contribution

It introduces a clustering-based enhancement to locally weighted expectations for Koopman generator estimation in stochastic systems, addressing noise and nonlinearity challenges.

Findings

Improved accuracy in estimating stochastic system dynamics.

Effective handling of nonlinear behavior through clustering.

Enhanced system identification performance demonstrated on a double-well potential.

Abstract

The estimation of equations from data is of interest in physics. One of the famous methods is the sparse identification of nonlinear dynamics (SINDy), which utilizes sparse estimation techniques to estimate equations from data. Recently, a method based on the Koopman operator has been developed; the generator extended dynamic mode decomposition (gEDMD) estimates a time evolution generator of dynamical and stochastic systems. However, a naive application of the gEDMD algorithm cannot work well for stochastic differential equations because of the noise effects in the data. Hence, the estimation based on conditional expectation values, in which we approximate the first and second derivatives on each coordinate, is practical. A naive approach is the usage of locally weighted expectations. We show that the naive locally weighted expectation is insufficient because of the nonlinear behavior of the underlying system. For improvement, we apply the clustering method in two ways; one is to reduce the effective number of data, and the other is to capture local information more accurately. We demonstrate the improvement of the proposed method for the double-well potential system with state-dependent noise.