Physics-Informed Machine Learning Meets HPC – SOTA and Challenges for Widespread Adoption
DescriptionHigh-fidelity simulations are increasingly important in the design of complex systems. However, the computational cost of such models hinders their use for design space exploration, optimization, and uncertainty quantification. Alternative approaches, such as projection-based methods, often exhibit limited accuracy and call for collecting simulations at several data points, which is expensive in the first place. Recently, however, research institutions and industry have been collaborating to develop physics-informed neural network frameworks for simulations. This BoF seeks input from the machine learning and HPC communities as well as open participation in the development of useful tools to meet their needs.
Event Type
Birds of a Feather
TimeThursday, 17 November 202212:15pm - 1:15pm CST
LocationC147-148-154
TP
XO/EX
Archive
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