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METHOD:PUBLISH
CALSCALE:GREGORIAN
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X-ORIGINAL-URL:https://matematica.unipv.it/
X-WR-CALNAME:Dipartimento di Matematica UNIPV
X-WR-CALDESC:
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-PUBLISHED-TTL:PT1H
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BEGIN:VEVENT
CLASS:PUBLIC
DTSTART;TZID=Europe/Rome:20220719T150000
DTEND;TZID=Europe/Rome:20220719T160000
DTSTAMP:20220709T152100
UID:MEC-1f81d18bb69fda4358a575aaf571c531@matematica.unipv.it
CREATED:20220709
LAST-MODIFIED:20220710
PRIORITY:5
TRANSP:OPAQUE
SUMMARY:Deep Neural Network Algorithms for Oscillatory Flows, Causality Operators and High Dimensional Fokker-Planck Equations
DESCRIPTION:Deep Neural Network Algorithms for Oscillatory Flows, Causality Operators and High Dimensional Fokker-Planck Equations\nWei Cai (SMU, Dallas, USA)\nAbstract: 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 causality DNN learning algorithm for nonlinear operators in highly oscillatory function spaces encountered in seismic wave responses and other evolution PDEs systems with causalities; (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.\n \n
URL:https://matematica.unipv.it/events/deep-neural-network-algorithms-for-oscillatory-flows-causality-operators-and-high-dimensional-fokker-planck-equations/
CATEGORIES:Seminari di matematica applicata
LOCATION:Aula Beltrami
END:VEVENT
END:VCALENDAR