Physics-constrained Machine learning

Thursday, March 8, 2018 - 9:00am - 10:00am
Karthik Duraisamy (University of Michigan)
Machine learning-driven models have achieved spectacular success in commercial applications such as language translation, speech and face recognition and bioinformatics. The natural question to ask then is: Can we bypass the traditional ways of intuition/hypothesis-driven model creation and instead use data to generate predictions of complex physics? This talk will begin with a discussion of the challenges of extending direct machine learning techniques to the prediction of physical phenomena.
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