Adaptive Uncertainty-Guided Model Selection for Data-Driven PDE Discovery

TL;DR

This paper introduces UBIC, a new criterion that combines uncertainty quantification and model complexity to improve PDE model selection from noisy data, validated by neural network simulations.

Contribution

The paper proposes UBIC, a novel model selection criterion that penalizes PDEs based on uncertainty and complexity, enhancing the discovery of true governing equations from noisy data.

Findings

UBIC effectively identifies true PDEs from noisy data.

Denoising observed data improves model selection accuracy.

Physics-informed neural networks validate the discovered PDEs.

Abstract

We propose a new parameter-adaptive uncertainty-penalized Bayesian information criterion (UBIC) to prioritize the parsimonious partial differential equation (PDE) that sufficiently governs noisy spatial-temporal observed data with few reliable terms. Since the naive use of the BIC for model selection has been known to yield an undesirable overfitted PDE, the UBIC penalizes the found PDE not only by its complexity but also the quantified uncertainty, derived from the model supports' coefficient of variation in a probabilistic view. We also introduce physics-informed neural network learning as a simulation-based approach to further validate the selected PDE flexibly against the other discovered PDE. Numerical results affirm the successful application of the UBIC in identifying the true governing PDE. Additionally, we reveal an interesting effect of denoising the observed data on improving the trade-off between the BIC score and model complexity. Code is available at https://github.com/Pongpisit-Thanasutives/UBIC.