Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning

We propose a unified data-driven reduced order model (ROM) that bridges the performance gap between linear and nonlinear manifold approaches. Deep learning ROM (DL-ROM) using deep-convolutional autoencoders (DC–AE) has been shown to capture nonlinear solution manifolds but fails to perform adequatel...

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Detalles Bibliográficos
Autores principales: Kadeethum, Teeratorn, Ballarin, Francesco, O’Malley, Daniel, Choi, Youngsoo, Bouklas, Nikolaos, Yoon, Hongkyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9712510/
https://www.ncbi.nlm.nih.gov/pubmed/36450820
http://dx.doi.org/10.1038/s41598-022-24545-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9712510/
https://www.ncbi.nlm.nih.gov/pubmed/36450820
http://dx.doi.org/10.1038/s41598-022-24545-3

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