Machine Learning for Prediction with Missing Dynamics

TL;DR

This paper introduces a machine learning framework to recover missing dynamical system components from data, with theoretical guarantees and demonstrated effectiveness across various scientific models, notably using recurrent neural networks.

Contribution

It formulates the missing dynamics recovery as a supervised learning problem and provides theoretical guarantees for convergence, showing the superiority of recurrent neural networks over kernel regression.

Findings

Recurrent neural networks outperform kernel regression in trajectory recovery.

The framework offers path-wise convergence guarantees for finite time.

Effective across models like atmospheric, quantum, and plasma systems.

Abstract

This article presents a general framework for recovering missing dynamical systems using available data and machine learning techniques. The proposed framework reformulates the prediction problem as a supervised learning problem to approximate a map that takes the memories of the resolved and identifiable unresolved variables to the missing components in the resolved dynamics. We demonstrate the effectiveness of the proposed framework with a theoretical guarantee of a path-wise convergence of the resolved variables up to finite time and numerical tests on prototypical models in various scientific domains. These include the 57-mode barotropic stress models with multiscale interactions that mimic the blocked and unblocked patterns observed in the atmosphere, the nonlinear Schr\"{o}dinger equation which found many applications in physics such as optics and Bose-Einstein-Condense, the Kuramoto-Sivashinsky equation which spatiotemporal chaotic pattern formation models trapped ion mode in plasma and phase dynamics in reaction-diffusion systems. While many machine learning techniques can be used to validate the proposed framework, we found that recurrent neural networks outperform kernel regression methods in terms of recovering the trajectory of the resolved components and the equilibrium one-point and two-point statistics. This superb performance suggests that recurrent neural networks are an effective tool for recovering the missing dynamics that involves approximation of high-dimensional functions.