Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting

TL;DR

The paper introduces APHYNITY, a framework that combines physical models with deep learning to improve forecasting of complex dynamical systems with partial knowledge, ensuring interpretability and better generalization.

Contribution

It proposes a novel decomposition method that separates physical and data-driven components, enhancing accuracy and interpretability in modeling complex dynamics.

Findings

Effective in reaction-diffusion, wave, and pendulum systems

Accurately forecasts system evolution with partial physical knowledge

Correctly identifies relevant physical parameters

Abstract

Forecasting complex dynamical phenomena in settings where only partial knowledge of their dynamics is available is a prevalent problem across various scientific fields. While purely data-driven approaches are arguably insufficient in this context, standard physical modeling based approaches tend to be over-simplistic, inducing non-negligible errors. In this work, we introduce the APHYNITY framework, a principled approach for augmenting incomplete physical dynamics described by differential equations with deep data-driven models. It consists in decomposing the dynamics into two components: a physical component accounting for the dynamics for which we have some prior knowledge, and a data-driven component accounting for errors of the physical model. The learning problem is carefully formulated such that the physical model explains as much of the data as possible, while the data-driven component only describes information that cannot be captured by the physical model, no more, no less. This not only provides the existence and uniqueness for this decomposition, but also ensures interpretability and benefits generalization. Experiments made on three important use cases, each representative of a different family of phenomena, i.e. reaction-diffusion equations, wave equations and the non-linear damped pendulum, show that APHYNITY can efficiently leverage approximate physical models to accurately forecast the evolution of the system and correctly identify relevant physical parameters. Code is available at https://github.com/yuan-yin/APHYNITY .