Reduced-order deep learning for flow dynamics. The interplay between deep learning and model reduction

dc.contributor.author	Wang, Min
dc.contributor.author	Cheung, Siu Wun
dc.contributor.author	Leung, Wing Tat
dc.contributor.author	Chung, Eric T
dc.contributor.author	Efendiev, Yalchin
dc.contributor.author	Wheeler, Mary
dc.date.accessioned	2020-04-05T16:08:39Z
dc.date.available	2020-04-05T16:08:39Z
dc.date.issued	2020-01
dc.date.updated	2020-04-05T16:08:37Z
dc.identifier.issn	0021-9991
dc.identifier.issn	1090-2716
dc.identifier.uri	https://hdl.handle.net/10161/20371
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Journal of Computational Physics
dc.relation.isversionof	10.1016/j.jcp.2019.108939
dc.subject	Science & Technology
dc.subject	Technology
dc.subject	Physical Sciences
dc.subject	Computer Science, Interdisciplinary Applications
dc.subject	Physics, Mathematical
dc.subject	Computer Science
dc.subject	Physics
dc.subject	Multiscale
dc.subject	Upscaling
dc.subject	Porous media
dc.subject	Deep learning
dc.subject	Dynamics
dc.subject	FINITE-ELEMENT METHODS
dc.subject	MULTISCALE MODEL
dc.subject	UNCERTAINTY QUANTIFICATION
dc.subject	WAVE-PROPAGATION
dc.subject	HOMOGENIZATION
dc.subject	EFFICIENT
dc.subject	NETWORKS
dc.title	Reduced-order deep learning for flow dynamics. The interplay between deep learning and model reduction
dc.type	Journal article
duke.contributor.orcid	Wang, Min\|0000-0002-5639-6345
pubs.begin-page	108939
pubs.end-page	108939
pubs.organisational-group	Trinity College of Arts & Sciences
pubs.organisational-group	Mathematics
pubs.organisational-group	Duke
pubs.publication-status	Published
pubs.volume	401

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