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Deep neural network algorithms for oscillatory flows and operators, and high dimensional Fokker-Planck equations

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Wei Cai (Southern Methodist University)
Monday 15 November 2021, 16:00-16:30
Seminar Room 1, Newton Institute.

If you have a question about this talk, please contact nobody.

MDLW03 - Deep learning and partial differential equations

In this talk, we will present results on new types of deep neural network (DNN) in the following areas: (a) a multi-scale DNN method for solving highly oscillatory Navier-Stokes flows in complex domains (b) a multiscale DNN learning algorithm for nonlinear operators in highly oscillatory function spaces encountered in seismic wave responses and forward and inverse problems of high frequency wave scattering; (c) a DNN based on forward and backward stochastic differential equations (FBSDEs) for high dimensional PDEs such as Fokker-Planck equations in statistical description of biochemical systems, with application to compute the committor functions and reaction rates in transition path sampling theory of complex chemical and biological systems.

This talk is part of the Isaac Newton Institute Seminar Series series.

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Deep neural network algorithms for oscillatory flows and operators, and high dimensional Fokker-Planck equations

📅 Download to calendar (vCal)

⚠️ Important: MDLW03 - Deep learning and partial differential equations

👤 Speaker: Wei Cai (Southern Methodist University)
📅 Date & Time: Monday 15 November 2021, 16:00 - 16:30
📍 Venue: Seminar Room 1, Newton Institute

Questions? Contact the organiser

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Series This talk is part of the Isaac Newton Institute Seminar Series series.

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Deep neural network algorithms for oscillatory flows and operators, and high dimensional Fokker-Planck equations

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Deep neural network algorithms for oscillatory flows and operators, and high dimensional Fokker-Planck equations

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Deep neural network algorithms for oscillatory flows and operators, and high dimensional Fokker-Planck equations

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