Non-intrusive operator inference for chaotic systems

Abstract

This work explores the physics-driven machine learning (ML) technique operator inference (OpInf) for predicting the state of chaotic dynamical systems. OpInf provides a nonintrusive approach to infer approximations of polynomial operators in reduced space without having access to the full order operators appearing in discretized models. Datasets for the physics systems are generated using conventional numerical solvers, and then, projected to a low-dimensional space via principal component analysis (PCA). In latent space, a least-squares problem is set to fit a quadratic polynomial operator, which is subsequently employed in a time-integration scheme in order to produce extrapolations in the same space. Once solved, the inverse PCA operation is applied to reconstruct the extrapolations in the original space. The quality of the OpInf predictions is assessed via the normalized root mean squared errormetric from which the valid prediction time (VPT) is computed. Numerical experiments considering the chaotic systems Lorenz 96 and the Kuramoto–Sivashinsky equation show promising forecasting capabilities of the OpInf reduced order models with VPT ranges that outperform state-of-the-art ML methods such as backpropagation and reservoir computing recurrent neural networks (Vlachas et al., 2020), as well as Markov neural operators (Li et al., 2021)