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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Vydané v:	arXiv.org
Hlavní autori:	Zhu, Yinhao, Zabaras, Nicholas
Médium:	Paper
Jazyk:	English
Vydavateľské údaje:	Ithaca Cornell University Library, arXiv.org 21.01.2018
Predmet:	Algorithms Artificial neural networks Bayesian analysis Computer simulation Computer vision Encoders-Decoders Gaussian process Machine learning Neural networks Parameter uncertainty Performance evaluation Permeability Propagation Regression models Statistical inference
ISSN:	2331-8422
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