Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models

Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models

Deep learning-based reduced order models (DL-ROMs) have been recently proposed to overcome common limitations shared by conventional ROMs–built, e.g., through proper orthogonal decomposition (POD)–when applied to nonlinear time-dependent parametrized PDEs. In particular, POD-DL-ROMs can achieve an e...

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Bibliographic Details
Main Authors: Stefania Fresca, Federico Fatone, Andrea Manzoni
Format: Article
Language:English
Published: AIMS Press 2023-11-01
Series:Mathematics in Engineering
Subjects:
reduced order modeling
deep learning
proper orthogonal decomposition
long-short term memory networks
time forecasting
parametrized pdes
Online Access:https://www.aimspress.com/article/doi/10.3934/mine.2023096?viewType=HTML

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https://www.aimspress.com/article/doi/10.3934/mine.2023096?viewType=HTML

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