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 …
D Loukrezis, U Römer… - International Journal for …, 2019 - dl.begellhouse.com
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 …
S Paul - arXiv preprint arXiv:2402.04711, 2024 - arxiv.org
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 …
M Stoyanov, CG Webster - International Journal for Uncertainty …, 2015 - dl.begellhouse.com
We develop a projection-based dimension reduction approach for partial differential equations with high-dimensional stochastic coefficients. This technique uses samples of the …
X Dong, X Wang, J Ouyang - Physics of Fluids, 2024 - pubs.aip.org
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 …