Learning Hidden Physics and System Parameters with Deep Operator Networks

TL;DR

This paper introduces DeepONet-based frameworks for discovering hidden physical laws and system parameters from sparse, noisy data, improving generalization and efficiency over existing methods.

Contribution

It presents two novel DeepONet-based approaches for hidden physics discovery and parameter inference, addressing limitations of prior data-driven methods.

Findings

Achieved high accuracy in PDE term discovery across diverse equations.

Successfully inferred system parameters from limited, noisy sensor data.

Demonstrated robustness and efficiency on benchmark physics problems.

Abstract

Discovering hidden physical laws and identifying governing system parameters from sparse observations are central challenges in computational science and engineering. Existing data-driven methods, such as physics-informed neural networks (PINNs) and sparse regression, are limited by their need for extensive retraining, sensitivity to noise, or inability to generalize across families of partial differential equations (PDEs). In this work, we introduce two complementary frameworks based on deep operator networks (DeepONet) to address these limitations. The first, termed the Deep Hidden Physics Operator (DHPO), extends hidden-physics modeling into the operator-learning paradigm, enabling the discovery of unknown PDE terms across diverse equation families by identifying the mapping of unknown physical operators. The second is a parameter identification framework that combines pretrained DeepONet with physics-informed inverse modeling to infer system parameters directly from sparse sensor data. We demonstrate the effectiveness of these approaches on benchmark problems, including the Reaction-Diffusion system, Burgers' equation, the 2D Heat equation, and 2D Helmholtz equation. Across all cases, the proposed methods achieve high accuracy, with relative solution errors on the order of O(10^-2) and parameter estimation errors on the order of O(10^-3), even under limited and noisy observations. By uniting operator learning with physics-informed modeling, this work offers a unified and data-efficient framework for physics discovery and parameter identification, paving the way for robust inverse modeling in complex dynamical systems.