Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems

TL;DR

Lift & Learn is a physics-informed machine learning approach that constructs low-dimensional models for large-scale nonlinear dynamical systems by leveraging governing equations and a lifting transformation to quadratic form.

Contribution

The paper introduces a novel lifting-based method that preserves system physics and improves robustness in reduced-order modeling of nonlinear dynamical systems.

Findings

Accurately captures system physics in lifted coordinates

Demonstrates robustness to input variations

Shows generalizability on neuron and fluid dynamics models

Abstract

We present Lift & Learn, a physics-informed method for learning low-dimensional models for large-scale dynamical systems. The method exploits knowledge of a system's governing equations to identify a coordinate transformation in which the system dynamics have quadratic structure. This transformation is called a lifting map because it often adds auxiliary variables to the system state. The lifting map is applied to data obtained by evaluating a model for the original nonlinear system. This lifted data is projected onto its leading principal components, and low-dimensional linear and quadratic matrix operators are fit to the lifted reduced data using a least-squares operator inference procedure. Analysis of our method shows that the Lift & Learn models are able to capture the system physics in the lifted coordinates at least as accurately as traditional intrusive model reduction approaches. This preservation of system physics makes the Lift & Learn models robust to changes in inputs. Numerical experiments on the FitzHugh-Nagumo neuron activation model and the compressible Euler equations demonstrate the generalizability of our model.