Continuous Methods : Adaptively intrusive reduced order model closure

Autor:	Menier, Emmanuel, Bucci, Michele Alessandro, Yagoubi, Mouadh, Mathelin, Lionel, Dairay, Thibault, Meunier, Raphael, Schoenauer, Marc
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Physics - Classical Physics
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2211.16999 Zobrazit plný text záznamu View this record from Arxiv