Discovering Interpretable Ordinary Differential Equations from Noisy Data

TL;DR

This paper introduces an unsupervised method to discover interpretable ordinary differential equations from noisy data, leveraging spline transformations and linear systems to accurately identify underlying physical models.

Contribution

It proposes a novel approach that finds approximate solutions and estimates ODE coefficients without regularization, enhancing interpretability and robustness to noise.

Findings

High accuracy in ODE discovery from noisy data

Promotes sparsity without regularization

Robustness to experimental noise

Abstract

The data-driven discovery of interpretable models approximating the underlying dynamics of a physical system has gained attraction in the past decade. Current approaches employ pre-specified functional forms or basis functions and often result in models that lack physical meaning and interpretability, let alone represent the true physics of the system. We propose an unsupervised parameter estimation methodology that first finds an approximate general solution, followed by a spline transformation to linearly estimate the coefficients of the governing ordinary differential equation (ODE). The approximate general solution is postulated using the same functional form as the analytical solution of a general homogeneous, linear, constant-coefficient ODE. An added advantage is its ability to produce a high-fidelity, smooth functional form even in the presence of noisy data. The spline approximation obtains gradient information from the functional form which are linearly independent and creates the basis of the gradient matrix. This gradient matrix is used in a linear system to find the coefficients of the ODEs. From the case studies, we observed that our modeling approach discovers ODEs with high accuracy and also promotes sparsity in the solution without using any regularization techniques. The methodology is also robust to noisy data and thus allows the integration of data-driven techniques into real experimental setting for data-driven learning of physical phenomena.