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Unsupervised Denoising and Super-Resolution of Vascular Flow Data by Physics-Informed Machine Learning.

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Bibliographic Details
Title: Unsupervised Denoising and Super-Resolution of Vascular Flow Data by Physics-Informed Machine Learning.
Authors: Sautory T; Department of Mechanical Engineering, University of California, Berkeley, CA 94501.; University of California, Berkeley., Shadden SC; Department of Mechanical Engineering, University of California, Berkeley, CA 94501.
Source: Journal of biomechanical engineering [J Biomech Eng] 2024 Sep 01; Vol. 146 (9).
Publication Type: Journal Article; Research Support, Non-U.S. Gov't
Journal Info: Publisher: American Society Of Mechanical Engineers Country of Publication: United States NLM ID: 7909584 Publication Model: Print Cited Medium: Internet ISSN: 1528-8951 (Electronic) Linking ISSN: 01480731 NLM ISO Abbreviation: J Biomech Eng Subsets: MEDLINE
Database: MEDLINE Ultimate
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: <searchLink fieldCode="AU" term="%22Sautory+T%22">Sautory T</searchLink>; Department of Mechanical Engineering, University of California, Berkeley, CA 94501.; University of California, Berkeley.<br /><searchLink fieldCode="AU" term="%22Shadden+SC%22">Shadden SC</searchLink>; Department of Mechanical Engineering, University of California, Berkeley, CA 94501.
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        Value: 10.1115/1.4065165
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      – Code: eng
        Text: English
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      – TitleFull: Unsupervised Denoising and Super-Resolution of Vascular Flow Data by Physics-Informed Machine Learning.
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            NameFull: Sautory T
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            NameFull: Shadden SC
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            – D: 01
              M: 09
              Text: 2024 Sep 01
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              Y: 2024
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              Value: 146
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              Value: 9
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