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SUMMARY:Learning and Learning to Solve PDEs - Bin Dong (Peking University)
DTSTART:20211117T103000Z
DTEND:20211117T110000Z
UID:TALK165421@talks.cam.ac.uk
DESCRIPTION:Deep learning continues to dominate machine learning and has b
 een successful in computer vision\, natural language processing\, etc. Its
  impact has now expanded to many research areas in science and engineering
 . In this talk\, I will mainly focus on some recent impacts of deep learni
 ng on computational mathematics. I will present our recent work on bridgin
 g deep neural networks with numerical differential equations\, and how it 
 may guide us in designing new models and algorithms for some scientific co
 mputing tasks. On the one hand\, I will present some of our works on the d
 esign of interpretable data-driven models for system identification and mo
 del reduction. On the other hand\, I will present our recent attempts at c
 ombining wisdom from numerical PDEs and machine learning to design data-dr
 iven solvers for PDEs and their applications in electromagnetic simulation
 .
LOCATION:Seminar Room 1\, Newton Institute
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