Uncertainty-Aware Sparse Identification of Dynamical Systems via Bayesian Model Averaging

TL;DR

This paper introduces a Bayesian sparse identification method for dynamical systems that quantifies uncertainty in model selection, improving the reliability of data-driven modeling especially with limited data.

Contribution

It develops a Bayesian framework combining sparse modeling and model averaging to infer interaction structures with uncertainty quantification.

Findings

Accurately recovers sparse interaction structures in oscillator networks.

Quantifies uncertainty for each candidate interaction and basis component.

Identifies effective functional components even when the true model is not in the assumed class.

Abstract

In many problems of data-driven modeling for dynamical systems, the governing equations are not known a priori and must be selected phenomenologically from a large set of candidate interactions and basis functions. In such situations, point estimates alone can be misleading, because multiple model components may explain the observed data comparably well, especially when the data are limited or the dynamics exhibit poor identifiability. Quantifying the uncertainty associated with model selection is therefore essential for constructing reliable dynamical models from data. In this work, we develop a Bayesian sparse identification framework for dynamical systems with coupled components, aimed at inferring both interaction structure and functional form together with principled uncertainty quantification. The proposed method combines sparse modeling with Bayesian model averaging, yielding posterior inclusion probabilities that quantify the credibility of each candidate interaction and basis component. Through numerical experiments on oscillator networks, we show that the framework accurately recovers sparse interaction structures with quantified uncertainty, including higher-order harmonic components, phase-lag effects, and multi-body interactions. We also demonstrate that, even in a phenomenological setting where the true governing equations are not contained in the assumed model class, the method can identify effective functional components with quantified uncertainty. These results highlight the importance of Bayesian uncertainty quantification in data-driven discovery of dynamical models.