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

Mikhael Tannous; Chady Ghnatios; Eivind Fonn; Trond Kvamsdal; Francisco Chinesta

doi:10.1186/s40323-025-00299-1

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

Mikhael Tannous, Chady Ghnatios, Eivind Fonn, Trond Kvamsdal, Francisco Chinesta

School of Engineering

Research output: Contribution to journal › Article › peer-review

Original language	English
Article number	1
Pages (from-to)	15
Number of pages	1
Journal	Advanced Modeling and Simulation in Engineering Sciences
Volume	12
Issue number	1
DOIs	https://doi.org/10.1186/s40323-025-00299-1
State	Published - Jul 2 2025

ASJC Scopus Subject Areas

Modeling and Simulation
Engineering (miscellaneous)
Computer Science Applications
Applied Mathematics

Keywords

Machine learning
Minimally intrusive approach
Reduced order modelling
Solid mechanics
Structural mechanics

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10.1186/s40323-025-00299-1

Cite this

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AU - Fonn, Eivind

AU - Kvamsdal, Trond

AU - Chinesta, Francisco

N1 - Publisher Copyright: © The Author(s) 2025.

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KW - Minimally intrusive approach

KW - Reduced order modelling

KW - Solid mechanics

KW - Structural mechanics

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

ASJC Scopus Subject Areas

Keywords

Access to Document

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