Decreasing the temporal complexity for nonlinear, implicit reduced-order models by forecasting

TL;DR

This paper introduces a forecasting approach using Gappy POD to generate better initial guesses for implicit reduced-order models, significantly reducing the number of linear-system solves and computational complexity during simulation.

Contribution

It proposes leveraging temporal behavior forecasting with Gappy POD to improve initial guesses in implicit reduced-order models, decreasing the number of linear solves needed.

Findings

Forecasting reduces Newton solver iterations.

Fewer linear-system solves in reduced-order simulations.

Improved computational efficiency demonstrated.

Abstract

Implicit numerical integration of nonlinear ODEs requires solving a system of nonlinear algebraic equations at each time step. Each of these systems is often solved by a Newton-like method, which incurs a sequence of linear-system solves. Most model-reduction techniques for nonlinear ODEs exploit knowledge of system's spatial behavior to reduce the computational complexity of each linear-system solve. However, the number of linear-system solves for the reduced-order simulation often remains roughly the same as that for the full-order simulation. We propose exploiting knowledge of the model's temporal behavior to 1) forecast the unknown variable of the reduced-order system of nonlinear equations at future time steps, and 2) use this forecast as an initial guess for the Newton-like solver during the reduced-order-model simulation. To compute the forecast, we propose using the Gappy POD technique. The goal is to generate an a ccurate initial guess so that the Newton solver requires many fewer iterations to converge, thereby decreasing the number of lin ear-system solves in the reduced-order-model simulation.