Solving PDEs with Deep Learning

Mon May 3, 2021 4:00 p.m.—5:00 p.m.
Yuehaw

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Yuehaw

Speaker

Yuehaw Khoo, University of Chicago

Deep neural-network provides an alternative method for compressing high-dimensional functions arising from partial differential equations (PDE). In this talk, we focus on using artificial neural-networks for solving PDEs in two ways: (1) Using neural-networks to represent mappings between PDE coefficients and solutions. (2) Constructing a solution space with neural-networks when solving for a PDE, and obtaining the neural-network parameterized solution via optimization. We apply the methods in scattering problems and when studying transition between states in stochastic systems.
Yuehaw Khoo’s website

Information:

3:40pm 4:00pm   Pre-Talk Tea, password 24.
4:00pm 5:00pm   Via Zoom, Password: 24