Conditional variational autoencoder with Gaussian process regression recognition for parametric models

In this article, we present a data-driven method for parametric models with noisy observation data. Gaussian process regression based reduced order modeling (GPR-based ROM) can realize fast online predictions without using equations in the offline stage. However, GPR-based ROM does not perform well...

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Bibliographic Details

Published in:	Journal of computational and applied mathematics Vol. 438; p. 115532
Main Authors:	Zhang, Xuehan, Jiang, Lijian
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.03.2024
Subjects:	Conditional variational autoencoder Gaussian process regression Parametric models Proper orthogonal decomposition Conditional variational autoencoder Gaussian process regression Proper orthogonal decomposition Parametric models
ISSN:	0377-0427, 1879-1778
Online Access:	Get full text
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