Výsledky vyhledávání - "Maître, Olivier P Le"

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Mean Mesh Adaptation for Efficient CFD Simulations with Operating Conditions Variability

Autor: Dornier, Hugo, Maître, Olivier P Le, Congedo, Pietro M, Din, Itham Salah El, Marty, Julien, Bourasseau, Sébastien

When numerically solving partial differential equations, for a given problem and operating condition, adaptive mesh refinement (AMR) has proven its efficiency to automatically build a discretization achieving a prescribed accuracy at low cost. Howeve

Externí odkaz: http://arxiv.org/abs/2412.01274

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A second-order accurate numerical scheme for a time-fractional Fokker-Planck equation

Autor: Mustapha, Kassem, Knio, Omar M., Maître, Olivier P. Le

A time-stepping $L1$ scheme for solving a time fractional Fokker-Planck equation of order $\alpha \in (0, 1)$, with a general driving force, is investigated. A stability bound for the semi-discrete solution is obtained for $\alpha\in(1/2,1)$ {via a n

Externí odkaz: http://arxiv.org/abs/2106.14146

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A surrogate-based optimal likelihood function for the Bayesian calibration of catalytic recombination in atmospheric entry protection materials

Autor: del Val, Anabel, Maître, Olivier P. Le, Chazot, Olivier, Magin, Thierry E., Congedo, Pietro M.

This work deals with the inference of catalytic recombination parameters from plasma wind tunnel experiments for reusable thermal protection materials. One of the critical factors affecting the performance of such materials is the contribution to the

Externí odkaz: http://arxiv.org/abs/2010.04649

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Optimal projection of observations in a Bayesian setting

Autor: Giraldi, Loïc, Maître, Olivier P. Le, Hoteit, Ibrahim, Knio, Omar M.

Optimal dimensionality reduction methods are proposed for the Bayesian inference of a Gaussian linear model with additive noise in presence of overabundant data. Three different optimal projections of the observations are proposed based on informatio

Externí odkaz: http://arxiv.org/abs/1709.06606

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Coordinate Transformation and Polynomial Chaos for the Bayesian Inference of a Gaussian Process with Parametrized Prior Covariance Function

Autor: Sraj, Ihab, Maître, Olivier P. Le, Knio, Omar M., Hoteit, Ibrahim

This paper addresses model dimensionality reduction for Bayesian inference based on prior Gaussian fields with uncertainty in the covariance function hyper-parameters. The dimensionality reduction is traditionally achieved using the Karhunen-\Loeve e

Externí odkaz: http://arxiv.org/abs/1501.03323

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