Scalable uncertainty quantification for deep operator networks using randomized priors

Yang, Yibo; Kissas, Georgios; Perdikaris, Paris

doi:10.1016/j.cma.2022.115399

Scalable uncertainty quantification for deep operator networks using randomized priors

Journal Article · 01 September 2022 · Computer Methods in Applied Mechanics and Engineering

DOI:https://doi.org/10.1016/j.cma.2022.115399· OSTI ID:1976978

Yang, Yibo; Kissas, Georgios;

Not provided.

Research Organization:: Raytheon Technologies Corp., Waltham, MA (United States); Univ. of Pennsylvania, Philadelphia, PA (United States)

Sponsoring Organization:: USDOE Advanced Research Projects Agency - Energy (ARPA-E)

DOE Contract Number:: AR0001201; SC0019116

OSTI ID:: 1976978

Journal Information:: Computer Methods in Applied Mechanics and Engineering, Vol. 399, Issue C; ISSN 0045-7825

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

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