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Conference Papers Year : 2022

Continuous Methods : Adaptively intrusive reduced order model closure

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.
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Dates and versions

hal-03879332 , version 1 (30-11-2022)

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Emmanuel Menier, Michele Alessandro Bucci, Mouadh Yagoubi, Lionel Mathelin, Thibault Dairay, et al.. Continuous Methods : Adaptively intrusive reduced order model closure. ICML 2022 - Workshop Continuous time methods for machine learning, Jul 2022, Baltimore, United States. ⟨hal-03879332⟩
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