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Scalable and efficient algorithms for the propagation of uncertainty from data through inference to prediction for large-scale problems, with application to flow of the Antarctic ice sheet

Crossref DOI link: https://doi.org/10.1016/j.jcp.2015.04.047

Published Print: 2015-09

Update policy: https://doi.org/10.1016/elsevier_cm_policy

Authors

Isaac, Tobin

Petra, Noemi

Stadler, Georg

Ghattas, Omar
Funding

Funding for this research was provided by:

Air Force Office of Scientific Research (FA9550-12-1-0484)

U.S. Department of Energy (DE-FG02-09ER25914)

U.S. Department of Energy (DE-FC02-13ER26128)

U.S. Department of Energy (DE-SC0010518)

National Science Foundation (CMS-1028889)

National Science Foundation (OPP-0941678)

Office of Science (DE-AC05-00OR22725)
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Text and Data Mining valid from 2015-09-01

Accepted Manuscript valid from 2016-05-15
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This article is maintained by: Elsevier

Article Title: Scalable and efficient algorithms for the propagation of uncertainty from data through inference to prediction for large-scale problems, with application to flow of the Antarctic ice sheet

Journal Title: Journal of Computational Physics

CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.jcp.2015.04.047

Content Type: article

Copyright: Copyright © 2015 Elsevier Inc. All rights reserved.

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