Learning governing physics from output only measurements

TL;DR

This paper introduces a novel stochastic framework that enables the discovery of governing physical laws solely from output measurements, effectively handling noise and data sparsity.

Contribution

It proposes a new method combining stochastic calculus, sparse learning, and Bayesian statistics to identify physics from output-only data, extending equation discovery to stochastic settings.

Findings

Effective in noisy, sparse, and incomplete data scenarios

Successfully applied to numerical examples with partial measurements

Demonstrates robustness and efficiency of the proposed approach

Abstract

Extracting governing physics from data is a key challenge in many areas of science and technology. The existing techniques for equations discovery are dependent on both input and state measurements; however, in practice, we only have access to the output measurements only. We here propose a novel framework for learning governing physics of dynamical system from output only measurements; this essentially transfers the physics discovery problem from the deterministic to the stochastic domain. The proposed approach models the input as a stochastic process and blends concepts of stochastic calculus, sparse learning algorithms, and Bayesian statistics. In particular, we combine sparsity promoting spike and slab prior, Bayes law, and Euler Maruyama scheme to identify the governing physics from data. The resulting model is highly efficient and works with sparse, noisy, and incomplete output measurements. The efficacy and robustness of the proposed approach is illustrated on several numerical examples involving both complete and partial state measurements. The results obtained indicate the potential of the proposed approach in identifying governing physics from output only measurement.