IEEE Access

Machine learning (ML) is redefining what is possible in data-intensive fields of science and engineering. However, applying ML to problems in the physical sciences comes with a unique set of challenges: scientists want physically interpretable models that can (i) generalize to predict previously unobserved behaviors, (ii) provide effective forecasting predictions (extrapolation), and (iii) be certifiable. Autonomous systems will necessarily interact with changing and uncertain environments, motivating the need for models that can accurately extrapolate based on physical principles (e.g. Newton's universal second law for classical mechanics, F = ma). Standard ML approaches have shown impressive performance for predicting dynamics in an interpolatory regime, but the resulting models often lack interpretability and fail to generalize. We build on a sparse regression framework that discovers governing dynamical systems models from data, selecting relevant terms in the dynamics from a library of possible functions. Our critically enabling innovation introduces a relaxed version of a sparse optimization framework that allows the use of non-convex sparsity promoting regularization functions and addresses three open challenges in scientific problems and data sets: (i) robust handling of outliers and corrupt data within noisy sensor measurements, (ii) parametric dependencies in candidate library functions, and (iii) the imposition of physical constraints. By explicitly addressing these open challenges, the integrated and unified algorithm developed provides a significant advancement over current state-of-the-art sparse model discovery methods. We show that the approach discovers parsimonious dynamical models on several example systems. This flexible approach can be tailored to the unique challenges associated with a wide range of applications and data sets, providing a powerful ML-based framework for learning governing models for physical systems from data.

A Unified Sparse Optimization Framework to Learn Parsimonious Physics-Informed Models From Data