Continuous Methods : Adaptively intrusive reduced order model closure
This addresses the challenge of inaccurate dynamics in reduced order modeling for industrial applications, representing an incremental improvement with a hybrid method.
The paper tackles the problem of reduced order models (ROMs) failing to accurately reproduce complex dynamics in industrial simulations by proposing a novel ROM correction approach using NeuralODEs and a time-continuous memory formulation, resulting in high accuracy while maintaining low computational costs.
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.