Multi-level convolutional autoencoder networks for parametric prediction of spatio-temporal dynamics

A data-driven framework is proposed towards the end of predictive modeling of complex spatio-temporal dynamics, leveraging nested non-linear manifolds. Three levels of neural networks are used, with the goal of predicting the future state of a system of interest in a parametric setting. A convolutio...

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Veröffentlicht in:	Computer methods in applied mechanics and engineering Jg. 372; S. 113379
Hauptverfasser:	Xu, Jiayang, Duraisamy, Karthik
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 01.12.2020 Elsevier BV
Schlagworte:	Autoencoders Convolutional neural networks Data-driven modeling Machine learning Mathematical models Model reduction Modelling Neural networks Nonlinear dynamics Parameters Prediction models Reduced order modeling Spatial data Training Wave propagation Data-driven modeling Convolutional neural networks Autoencoders Reduced order modeling Machine learning
ISSN:	0045-7825, 1879-2138
Online-Zugang:	Volltext
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