CEE Seminar: Data-Driven probabilistic Learning on Manifolds and Nonconvex Optimization Problems with Afpplications

Apr 24

Tuesday, April 24, 2018

12:00 pm - 1:00 pm
Fitzpatrick Center Schiciano Auditorium Side A, room 1464

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Presenter

Professor Christian Soize, Professor, Multi Scale Modeling and Simulation Laboratory, Mechanics Team, Université Paris-Est Marne-la-Vallée

The talk will be devoted to the presentation of a novel approach concerning data-driven probabilitistic learning on manifolds with applications in computational mechanics. This tool of the computational statistics can be viewed as a useful method in scientific machine learning based on the probability theory. We first explaining the concept/method of this probabilistic learning on manifolds by discussing a challenging problem of nonconvex optimization under uncertainties (OUU). We will then present the mathematical formulation and the main steps of the method based on the construction of a diffusion-maps basis and the projection on it of a nonlinear Itô stochastic differential equation. After having presented two simple illustrations, fours applications will be presented: -Optimization under uncertainties using a limited number of function evaluations. -Enhancing model predictability for a scramjet using probabilistic learning on manifolds. -Design optimization under uncertainties of a mesoscale implant in biological tissues using probabilistic learning. -Probabilistic learning onmanifolds for nonparametric probabilistic approach of model-form uncertainties in nonlinear computational mechanics.

Contact

Carpenter, Ruby Nell
660-5200
rubync@duke.edu

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