Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification

We are interested in the development of surrogate models for uncertainty quantification and propagation in problems governed by stochastic PDEs using a deep convolutional encoder–decoder network in a similar fashion to approaches considered in deep learning for image-to-image regression tasks. Since...

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Veröffentlicht in:	Journal of computational physics Jg. 366; S. 415 - 447
Hauptverfasser:	Zhu, Yinhao, Zabaras, Nicholas
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cambridge Elsevier Inc 01.08.2018 Elsevier Science Ltd
Schlagworte:	Algorithms Artificial neural networks Bayesian analysis Bayesian neural networks Computational physics Computer simulation Computer vision Convolutional encoder–decoder networks Deep learning Gaussian process Machine learning Neural networks Parameter uncertainty Permeability Porous media flows Propagation Regression models Statistical inference Stochastic models Training Uncertainty quantification Deep learning Uncertainty quantification Porous media flows Convolutional encoder–decoder networks Bayesian neural networks
ISSN:	0021-9991, 1090-2716
Online-Zugang:	Volltext
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