# A unified sparse optimization framework to learn parsimonious physics-informed models from data

## TL;DR

This paper presents a unified sparse optimization framework for learning interpretable, physically consistent dynamical models from data, capable of generalizing beyond observed regimes and handling real-world challenges.

## Contribution

It introduces a flexible, non-convex sparse optimization method that learns parsimonious, interpretable models with physical constraints from diverse datasets.

## Key findings

- Successfully discovers simple dynamical models in example systems.
- Handles outliers, parametric dependencies, and physical constraints.
- Models generalize well to new parameter regimes.

## Abstract

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 introduce a unified sparse optimization framework that learns governing dynamical systems models from data, selecting relevant terms in the dynamics from a library of possible functions. The resulting models are parsimonious, have physical interpretations, and can generalize to new parameter regimes. Our framework allows the use of non-convex sparsity promoting regularization functions and can be adapted to address key challenges in scientific problems and data sets, including outliers, parametric dependencies, and physical constraints. 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.