Nathalie Bartoli
University of Toulouse
82 Papers
103 Citations
Nathalie Bartoli is an academic researcher from University of Toulouse. The author has contributed to research in topics: Computer science & Surrogate model. The author has an hindex of 14, co-authored 64 publications. Previous affiliations of Nathalie Bartoli include Centre national de la recherche scientifique.
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Papers
Enhancing the handling qualities analysis by collaborative aerodynamics surrogate modelling and aero-data fusion
TL;DR: The test case shows that by combining a collaborative surrogate modeling approach with fusion of the data sets, the fidelity of the analysis data can be significantly improved giving maximum relative prediction error less than 5 % with minimal computing efforts.
SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian Processes
P Saves,Rémi Lafage,Nathalie Bartoli,Youssef Diouane,Jasper H. Bussemaker,Thierry Lefebvre,John T. Hwang,Joseph Morlier,Joaquim R. R. A. Martins +8 more
TL;DR: The Surrogate Modeling Toolbox (SMT) as mentioned in this paper is an open-source Python package that offers a collection of surrogate modeling methods, sampling techniques, and a set of sample problems.
Extreme value oriented random field discretization based on an hybrid polynomial chaos expansion — Kriging approach
TL;DR: An adaptive approach for the discretization of the random field modeling the quantity of interest is developed, to focus the computational budget over the areas of the parametric space where the minimum or the maximum of the field is likely to be for any realization of the stochastic parameters.
Industrial Application of an Advanced Bi-level MDO Formulation to Aircraft Engine Pylon Optimization
Anne Gazaix,François Gallard,Vincent Ambert,Damien Guénot,Maxime Hamadi,Stéphane Grihon,Patrick Sarouille,Thierry Y. Druot,Joel Brezillon,Vincent Gachelin,Justin Plakoo,Nicolas Desfachelles,Nathalie Bartoli,Thierry Lefebvre,Selime Gürol,Benoit Pauwels,Charlie Vanaret,Rémi Lafage +17 more
- 17 Jun 2019
Adaptive coupling of reduced basis modeling and Kriging based active learning methods for reliability analyses
TL;DR: A method combining such adaptive sampling based reliability analyses and reduced basis modeling is proposed using on an efficient coupling criterion and enabled significant computational cost reductions, while ensuring accurate estimations of failure probabilities.