SNF-ROM: Projection-based nonlinear reduced order modeling with smooth neural fields

TL;DR

SNF-ROM introduces a nonlinear reduced order modeling framework using smooth neural fields and Galerkin projection, significantly improving accuracy and computational speed for PDEs, especially in advection-dominated flows.

Contribution

The paper proposes SNF-ROM, a novel nonlinear ROM that combines smooth neural fields with Galerkin projection, enhancing stability and efficiency over traditional linear models.

Findings

Achieves up to 199x speedup over full-order models.

Outperforms state-of-the-art ROMs on various PDE problems.

Ensures smooth, differentiable neural fields for stable dynamics.

Abstract

Reduced order modeling lowers the computational cost of solving PDEs by learning a low-order spatial representation from data and dynamically evolving these representations using manifold projections of the governing equations. While commonly used, linear subspace reduced-order models (ROMs) are often suboptimal for problems with a slow decay of Kolmogorov $n$-width, such as advection-dominated fluid flows at high Reynolds numbers. There has been a growing interest in nonlinear ROMs that use state-of-the-art representation learning techniques to accurately capture such phenomena with fewer degrees of freedom. We propose smooth neural field ROM (SNF-ROM), a nonlinear reduced modeling framework that combines grid-free reduced representations with Galerkin projection. The SNF-ROM architecture constrains the learned ROM trajectories to a smoothly varying path, which proves beneficial in the dynamics evaluation when the reduced manifold is traversed in accordance with the governing PDEs. Furthermore, we devise robust regularization schemes to ensure the learned neural fields are smooth and differentiable. This allows us to compute physics-based dynamics of the reduced system nonintrusively with automatic differentiation and evolve the reduced system with classical time-integrators. SNF-ROM leads to fast offline training as well as enhanced accuracy and stability during the online dynamics evaluation. Numerical experiments reveal that SNF-ROM is able to accelerate the full-order computation by up to $199\times$. We demonstrate the efficacy of SNF-ROM on a range of advection-dominated linear and nonlinear PDE problems where we consistently outperform state-of-the-art ROMs.