Learning functional priors and posteriors from data and physics is a research paper published in Journal of Computational Physics (2022). On theSindex it has a DataRank of 0.606. It has been cited 56 times.
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Base Score Contribution
0.606
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
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Grant: FA9550-20-1-0358
National Institutes of Health
Grant: U01 HL142518
U.S. Department of Energy
Grant: DE-SC0019453
National Institutes of Health
Grant: 5U01HL142518-03
Multimodality imaging-driven multifidelity modeling of aortic dissection
FWCI
5.43
Citation Percentile
1.0%
Citation Trend
Fields of Study
Keywords
Sustainable Development Goals
Learning Functional Priors and Posteriors from Data and Physics