Continuous Methods : Adaptively intrusive reduced order model closure

TL;DR

This paper introduces a neural network-based correction method for reduced order models using NeuralODEs, improving accuracy in complex dynamic simulations while maintaining low computational costs.

Contribution

It presents a novel ROM correction approach leveraging NeuralODEs with a continuous memory formulation, enhancing accuracy without increasing computational costs.

Findings

High accuracy in complex dynamics simulations

Retains low computational costs of reduced models

Effective correction method demonstrated experimentally

Abstract

Reduced order modeling methods are often used as a mean to reduce simulation costs in industrial applications. Despite their computational advantages, reduced order models (ROMs) often fail to accurately reproduce complex dynamics encountered in real life applications. To address this challenge, we leverage NeuralODEs to propose a novel ROM correction approach based on a time-continuous memory formulation. Finally, experimental results show that our proposed method provides a high level of accuracy while retaining the low computational costs inherent to reduced models.