Machine Learning for Prediction with Missing Dynamics
This work addresses the challenge of incomplete dynamical modeling in scientific domains such as atmospheric science and physics, offering a general framework with theoretical guarantees, though it appears incremental as it builds on existing machine learning techniques.
The authors tackled the problem of predicting missing dynamical systems by reformulating it as a supervised learning task to approximate a map from resolved and unresolved variables to missing components, achieving path-wise convergence up to finite time with numerical validation on models like barotropic stress and Kuramoto-Sivashinsky equations.
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 Schrödinger 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.