Reduced-order modeling for stochastic large-scale and time-dependent flow problems using deep spatial and temporal convolutional autoencoders

A non-intrusive reduced-order model based on convolutional autoencoders is proposed as a data-driven tool to build an efficient nonlinear reduced-order model for stochastic spatiotemporal large-scale flow problems. The objective is to perform accurate and rapid uncertainty analyses of the flow outpu...

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Veröffentlicht in:	Advanced modeling and simulation in engineering sciences Jg. 10; H. 1; S. 7 - 27
Hauptverfasser:	Abdedou, Azzedine, Soulaimani, Azzeddine
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 19.05.2023 Springer Nature B.V Springer SpringerOpen
Schlagworte:	Accuracy Artificial neural networks Classical and Continuum Physics Computational Science and Engineering Convolutional autoencoders Deep learning Engineering Engineering Sciences Finite element method Mathematical models Multilayer perceptrons Parameter uncertainty Partial differential equations Proper Orthogonal Decomposition Reduced order models Reduced-order modeling Research Article Solvers Statistical analysis Theoretical and Applied Mechanics Time compression Time dependence Uncertainty analysis Uncertainty propagation Deep learning Uncertainty propagation Convolutional autoencoders Reduced-order modeling
ISSN:	2213-7467, 2213-7467
Online-Zugang:	Volltext
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