Data-driven Identification of Parametric Governing Equations of Dynamical Systems Using the Signed Cumulative Distribution Transform

TL;DR

This paper introduces a new data-driven method using the signed cumulative distribution transform (SCDT) to accurately identify PDE parameters in dynamical systems, including damage detection, without prior excitation knowledge.

Contribution

The paper develops the SCDT-based linear regression approach for PDE parameter estimation, improving accuracy and applicability in structural health monitoring over existing machine learning methods.

Findings

Superior accuracy in PDE parameter estimation compared to recent ML methods.

Effective damage detection demonstrated on real SHM dataset.

No need for prior knowledge of excitation sources or initial conditions.

Abstract

This paper presents a novel data-driven approach to identify partial differential equation (PDE) parameters of a dynamical system. Specifically, we adopt a mathematical "transport" model for the solution of the dynamical system at specific spatial locations that allows us to accurately estimate the model parameters, including those associated with structural damage. This is accomplished by means of a newly-developed mathematical transform, the signed cumulative distribution transform (SCDT), which is shown to convert the general nonlinear parameter estimation problem into a simple linear regression. This approach has the additional practical advantage of requiring no a priori knowledge of the source of the excitation (or, alternatively, the initial conditions). By using training data, we devise a coarse regression procedure to recover different PDE parameters from the PDE solution measured at a single location. Numerical experiments show that the proposed regression procedure is capable of detecting and estimating PDE parameters with superior accuracy compared to a number of recently developed machine learning methods. Furthermore, a damage identification experiment conducted on a publicly available dataset provides strong evidence of the proposed method's effectiveness in structural health monitoring (SHM) applications. The Python implementation of the proposed system identification technique is integrated as a part of the software package PyTransKit (https://github.com/rohdelab/PyTransKit).