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SUMMARY:Bin Dong (BICMR\, Peking University)
DTSTART:20201111T110000Z
DTEND:20201111T120000Z
DTSTAMP:20260423T003237Z
UID:MPML/22
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/MPML/22/">Le
 arning and Learning to Solve PDEs</a>\nby Bin Dong (BICMR\, Peking Univers
 ity) as part of Mathematics\, Physics and Machine Learning (IST\, Lisbon)\
 n\n\nAbstract\nDeep learning continues to dominate machine learning and ha
 s been successful in computer vision\, natural language processing\, etc. 
 Its impact has now expanded to many research areas in science and engineer
 ing. In this talk\, I will mainly focus on some recent impact of deep lear
 ning on computational mathematics. I will present our recent work on bridg
 ing deep neural networks with numerical differential equations. On the one
  hand\, I will show how to design transparent deep convolutional networks 
 to uncover hidden PDE models from observed dynamical data. On the other ha
 nd\, I will present our preliminary attempt to establish a deep reinforcem
 ent learning based framework to solve 1D scalar conservation laws\, and a 
 meta-learning approach for solving linear parameterized PDEs based on the 
 multigrid method.\n
LOCATION:https://researchseminars.org/talk/MPML/22/
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