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DTSTAMP:20230124T170829Z
LOCATION:C147-148-154
DTSTART;TZID=America/Chicago:20221117T121500
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UID:submissions.supercomputing.org_SC22_sess370@linklings.com
SUMMARY:Physics-Informed Machine Learning Meets HPC – SOTA and Challenges 
 for Widespread Adoption
DESCRIPTION:Birds of a Feather\n\nPhysics-Informed Machine Learning Meets 
 HPC – SOTA and Challenges for Widespread Adoption\n\nViana, Tadepalli, Cor
 betta, Wright, Chauhan...\n\nHigh-fidelity simulations are increasingly im
 portant in the design of complex systems. However, the computational cost 
 of such models hinders their use for design space exploration, optimizatio
 n, and uncertainty quantification. Alternative approaches, such as project
 ion-based methods, often exhibit ...\n\n\nRegistration Category: Tech Prog
 ram Reg Pass, Exhibits Reg Pass
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