Article Dans Une Revue Advanced Modeling and Simulation in Engineering Sciences Année : 2025

Machine learning (ML) based reduced order modelling (ROM) for linear and non-linear solid and structural mechanics

Résumé

Multiple model reduction techniques have been proposed to tackle linear and non-linear problems. Intrusive model order reduction techniques exhibit high accuracy levels; however, they are rarely used as a standalone industrial tool because of the high level of expertise required in the construction and usage of these techniques. Moreover, the computation time benefit is compromised for highly non-linear problems. On the other hand, non-intrusive methods often struggle with accuracy in non-linear cases, typically requiring a large design of experiments and a large number of snapshots in order to achieve reliable performance. However, generating the stiffness matrix in a non-intrusive approach presents an optimal way to align accuracy with efficiency, combining the advantages of both intrusive and non-intrusive methods. This work introduces a minimally intrusive model order reduction technique that employs machine learning within a Proper Orthogonal Decomposition framework to achieve this alliance. By leveraging outputs from commercial full-order models, this method constructs a reduced-order model that operates effectively without requiring expert user intervention. The proposed technique is capable of approximating linear non-affine as well as non-linear terms. It is showcased for linear and non-linear structural mechanics problems.

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hal-05547841 , version 1 (11-03-2026)

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Mikhael Tannous, Chady Ghnatios, Eivind Fonn, Trond Kvamsdal, Francisco Chinesta. Machine learning (ML) based reduced order modelling (ROM) for linear and non-linear solid and structural mechanics. Advanced Modeling and Simulation in Engineering Sciences, 2025, 12, ⟨10.1186/s40323-025-00299-1⟩. ⟨hal-05547841⟩
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