Gaussian Process Assisted Active Learning of Physical Laws

TL;DR

This paper introduces an active learning method using Gaussian processes to efficiently discover differential equations from noisy data, reducing experimental costs.

Contribution

It combines D-optimality and space-filling criteria with Gaussian process derivatives to improve differential equation discovery accuracy and data efficiency.

Findings

Outperforms traditional design methods in accuracy.

Reduces required experimental data for reliable modeling.

Demonstrates effectiveness across multiple case studies.

Abstract

In many areas of science and engineering, discovering the governing differential equations from the noisy experimental data is an essential challenge. It is also a critical step in understanding the physical phenomena and prediction of the future behaviors of the systems. However, in many cases, it is expensive or time-consuming to collect experimental data. This article provides an active learning approach to estimate the unknown differential equations accurately with reduced experimental data size. We propose an adaptive design criterion combining the D-optimality and the maximin space-filling criterion. In contrast to active learning for other regression models, the D-optimality here requires the unknown solution of the differential equations and derivatives of the solution. We estimate the Gaussian process (GP) regression models from the available experimental data and use them as the surrogates of these unknown solution functions. The derivatives of the estimated GP models are derived and used to substitute the derivatives of the solution. Variable selection-based regression methods are used to learn the differential equations from the experimental data. Through multiple case studies, we demonstrate the proposed approach outperforms the D-optimality and the maximin space-filling design alone in terms of model accuracy and data economy.