Physics-informed Discovery of State Variables in Second-Order and Hamiltonian Systems

TL;DR

This paper introduces a physics-informed neural network approach for discovering minimal, interpretable state variables in second-order Hamiltonian systems, improving over purely data-driven methods by incorporating physical constraints.

Contribution

It presents a novel method that leverages physical properties of Hamiltonian systems to enhance state variable discovery, addressing limitations of prior data-driven models.

Findings

Outperforms baseline models in identifying minimal state variables

Produces more interpretable and physically consistent state representations

Demonstrates effectiveness on second-order Hamiltonian systems

Abstract

The modeling of dynamical systems is a pervasive concern for not only describing but also predicting and controlling natural phenomena and engineered systems. Current data-driven approaches often assume prior knowledge of the relevant state variables or result in overparameterized state spaces. Boyuan Chen and his co-authors proposed a neural network model that estimates the degrees of freedom and attempts to discover the state variables of a dynamical system. Despite its innovative approach, this baseline model lacks a connection to the physical principles governing the systems it analyzes, leading to unreliable state variables. This research proposes a method that leverages the physical characteristics of second-order Hamiltonian systems to constrain the baseline model. The proposed model outperforms the baseline model in identifying a minimal set of non-redundant and interpretable state variables.