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SUMMARY:Kohn-Sham equations as regularizer: building prior knowledge into 
 machine-learned physics - Li Li\, Google Research
DTSTART:20210517T153000Z
DTEND:20210517T163000Z
UID:TALK160360@talks.cam.ac.uk
CONTACT:Bingqing Cheng
DESCRIPTION:Including prior knowledge is important for effective machine l
 earning models in physics\, and is usually achieved by explicitly adding l
 oss terms or constraints on model architectures. Prior knowledge embedded 
 in the physics computation itself rarely draws attention. We consider the 
 Kohn-Sham self-consistent calculation as a differentiable program and show
  that solving the Kohn-Sham equations when training neural networks for th
 e exchange-correlation functional provides an implicit regularization that
  greatly improves generalization.  Our results serve as proof of principle
  for rethinking computational physics in this emerging paradigm of scienti
 fic computing with enormous development in automatic differentiation libra
 ries\, hardware accelerators and deep learning.
LOCATION:virtual ZOOM meeting ID: 263 591 6003\, Passcode: 000042\, https:
 //us02web.zoom.us/j/2635916003?pwd=ZlBEQnRENGwxNmJGMENGMWxjak5nUT09
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