Modeling Unknown Stochastic Dynamical System Subject to External Excitation

TL;DR

This paper introduces a numerical method to learn and predict unknown nonautonomous stochastic dynamical systems from limited input/output data, enabling accurate long-term response predictions under arbitrary excitations.

Contribution

The paper proposes a novel approach combining local data approximation and generative modeling to learn unknown stochastic dynamics from partial I/O data.

Findings

Method accurately predicts stochastic responses under new excitations.

Effective for long-term system response prediction.

Demonstrated success on various numerical examples.

Abstract

We present a numerical method for learning unknown nonautonomous stochastic dynamical system, i.e., stochastic system subject to time dependent excitation or control signals. Our basic assumption is that the governing equations for the stochastic system are unavailable. However, short bursts of input/output (I/O) data consisting of certain known excitation signals and their corresponding system responses are available. When a sufficient amount of such I/O data are available, our method is capable of learning the unknown dynamics and producing an accurate predictive model for the stochastic responses of the system subject to arbitrary excitation signals not in the training data. Our method has two key components: (1) a local approximation of the training I/O data to transfer the learning into a parameterized form; and (2) a generative model to approximate the underlying unknown stochastic flow map in distribution. After presenting the method in detail, we present a comprehensive set of numerical examples to demonstrate the performance of the proposed method, especially for long-term system predictions.