Uncertainty quantification in complex physical models is often challenged by the
computational expense of these models. One often needs to operate under the assumption …

Reduced order modeling is an important and fast-growing research field in computational
science and engineering, motivated by several reasons, of which we mention just a few …

This research compares a forward-mounted overwing nacelle configuration to a
conventional underwing nacelle for a single-aisle transport aircraft. We focus on …

We consider the problem of quantifying uncertainty regarding the output of an
electromagnetic field problem, in the presence of a large number of uncertain input …

Design optimization of engineering systems with multiple competing objectives is a
painstakingly tedious process especially when the objective functions are expensive-to …

Résumé D e nos jours, un intérêt significatif et croissant pour améliorer les processus de
conception de véhicules s' observe dans le domaine de l'optimisation multidisciplinaire …

We develop a projection-based dimension reduction approach for partial differential
equations with high-dimensional stochastic coefficients. This technique uses samples of the …

This paper presents an active learning smoothed particle hydrodynamics (ALSPH) method
to simulate generalized Newtonian free surface flows. First, an improved smoothed particle …

In the early stages of aerospace design, reduced-order models (ROMs) are crucial for
minimizing computational costs associated with using physics-rich field information in many …

Description: Ph. D on Gaussian Process kernels for Bayesian optimization in high dimension
with mixed and hierarchical variables at ISAE-SUPAERO. Keywords: Gaussian process …