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Título: " Probabilistic learning for Uncertainty Quantification in computational sciences and engineering"

Palestrante: Prof. C. Soize, Laboratoire MSME, UMR 8208 Université Paris-Est Marne-la-Vallée, France.

DATA: 24 de agosto de 2018, sexta-feira
LOCAL: Centro de Tecnologia, Bloco I-241, Ilha do Fundão


The talk will be devoted to the presentation of a novel approach concerning data driven and probabilistic 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 explain 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 on manifolds for nonparametric probabilistic approach of model form uncertainties in nonlinear computational mechanics.

Short Biography:

He has received many awards and honors, among which, ‘’Noury Prize awarded by the French Academy of Sciences’’, 1985 and ’’IACM Award Computational Mechanic’’ delivered by the International Association for Computational Mechanics (2018).  He does research in:
- Stochastic modeling of uncertainties in computational mechanics, their propagation and their quantification solving stochastic inverse problems.
- Statistical Learning, Probabilistic Learning, Machine Learning, and Nonconvex Optimization Problem.
- Stochastic multi-scale modeling and application to microstructures of heterogeneous materials.
- Computational mechanics, linear and nonlinear structural dynamics, structural acoustics, vibroacoustics and coupled  systems.
- Computational stochastic dynamics for linear and nonlinear dynamical systems.



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