Discover governing differential equations from evolving systems

TL;DR

This paper introduces an online method for discovering governing differential equations from streaming data, effectively handling evolving systems and identifying change points in real-time.

Contribution

It proposes a novel online modeling approach that processes streaming data sequentially, enabling real-time discovery of differential equations and change point detection in evolving systems.

Findings

Effective in discovering ODEs and PDEs from streaming data

Accurately identifies change points in hybrid and switching systems

Competitive performance compared to existing methods

Abstract

Discovering the governing equations of evolving systems from available observations is essential and challenging. In this paper, we consider a new scenario: discovering governing equations from streaming data. Current methods struggle to discover governing differential equations with considering measurements as a whole, leading to failure to handle this task. We propose an online modeling method capable of handling samples one by one sequentially by modeling streaming data instead of processing the entire dataset. The proposed method performs well in discovering ordinary differential equations (ODEs) and partial differential equations (PDEs) from streaming data. Evolving systems are changing over time, which invariably changes with system status. Thus, finding the exact change points is critical. The measurement generated from a changed system is distributed dissimilarly to before; hence, the difference can be identified by the proposed method. Our proposal is competitive in identifying the change points and discovering governing differential equations in three hybrid systems and two switching linear systems.