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:	arXiv.org
Hauptverfasser:	Zhu, Yinhao, Zabaras, Nicholas
Format:	Paper
Sprache:	Englisch
Veröffentlicht:	Ithaca Cornell University Library, arXiv.org 21.01.2018
Schlagworte:	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
Online-Zugang:	Volltext
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