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Description:: This thesis takes aim at two directions. The first direction involves setting the foundations for a new type of data-driven scientific computing, essentially creating a …
Year:: 2015
Contributor:: Perdikaris, Paris (creator); Karniadakis, George (Director); Royset, Johannes (Reader); Venturi, Daniele (Reader); Brown University. Applied Mathematics (sponsor)
Genre:: theses
Subject:: Multi-fidelity modeling; High performance scientific computing; Design optimization; Uncertainty quantification; Arterial biomechanics; Inverse problems; Machine learning; Big data; Blood flow
Language:: English
Collection:: Applied Mathematics; Theses and Dissertations

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