Reduced-Order Modeling of Subsurface Multi-phase Flow Models Using Deep Residual Recurrent Neural Ne...
Reduced-Order Modeling of Subsurface Multi-phase Flow Models Using Deep Residual Recurrent Neural Networks
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Publisher
Dordrecht: Springer Netherlands
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Language
English
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Dordrecht: Springer Netherlands
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Contents
We present a reduced-order modeling technique for subsurface multi-phase flow problems building on the recently introduced deep residual recurrent neural network (DR-RNN) (Nagoor Kani et al. in DR-RNN: a deep residual recurrent neural network for model reduction. ArXiv e-prints,
2017
). DR-RNN is a physics-aware recurrent neural network for m...
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Full title
Reduced-Order Modeling of Subsurface Multi-phase Flow Models Using Deep Residual Recurrent Neural Networks
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TN_cdi_proquest_journals_2344554753
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2344554753
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ISSN
0169-3913
E-ISSN
1573-1634
DOI
10.1007/s11242-018-1170-7
