Title: Differentiate Everything: a lesson from deep learning
Abstract: Deep learning taught us a new way to play with computers: compose differentiable components into a computer program, then tune its parameters via gradient optimization until that program achieves what we want. This is the key idea of differentiable programming. The rapid development of deep learning technology offers convenient tools for differentiable programming, and also opens a new frontier for computational physics. I will introduce the basic notions of differentiable programming and its physics applications including modelling, optimization, control, and inverse design.
Talk – Video
Talk – Slides
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Last Updated: 19th November 2020 by Denjoe O'Connor
Lei Wang (Chinese Academy of Sciences, Beijing)
Title: Differentiate Everything: a lesson from deep learning
Abstract: Deep learning taught us a new way to play with computers: compose differentiable components into a computer program, then tune its parameters via gradient optimization until that program achieves what we want. This is the key idea of differentiable programming. The rapid development of deep learning technology offers convenient tools for differentiable programming, and also opens a new frontier for computational physics. I will introduce the basic notions of differentiable programming and its physics applications including modelling, optimization, control, and inverse design.
Talk – Video
Talk – Slides
Category: Uncategorised
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