Non-intrusive surrogate modeling for parametrized time-dependent partial differential equations using convolutional autoencoders

This paper presents a novel non-intrusive surrogate modeling scheme based on deep learning for predictive modeling of complex systems, described by parametrized time-dependent partial differential equations. Specifically, the proposed method utilizes a convolutional autoencoder in conjunction with a...

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Veröffentlicht in:	Engineering applications of artificial intelligence Jg. 109; S. 104652
Hauptverfasser:	Nikolopoulos, Stefanos, Kalogeris, Ioannis, Papadopoulos, Vissarion
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.03.2022
Schlagworte:	Convolutional autoencoders Deep learning Feed forward neural networks Monte Carlo simulation Surrogate modeling Deep learning Convolutional autoencoders Surrogate modeling Feed forward neural networks Monte Carlo simulation
ISSN:	0952-1976, 1873-6769
Online-Zugang:	Volltext
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