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Nathalie Bartoli
Researcher at University of Toulouse
Publications - 79
Citations - 1094
Nathalie Bartoli is an academic researcher from University of Toulouse. The author has contributed to research in topics: Computer science & Optimization problem. The author has an hindex of 14, co-authored 64 publications receiving 650 citations. Previous affiliations of Nathalie Bartoli include Centre national de la recherche scientifique.
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Journal ArticleDOI
A Python surrogate modeling framework with derivatives
Mohamed Amine Bouhlel,John T. Hwang,Nathalie Bartoli,Rémi Lafage,Joseph Morlier,Joaquim R. R. A. Martins +5 more
TL;DR: The surrogate modeling toolbox (SMT) is an open-source Python package that contains a collection of surrogate modeling methods, sampling techniques, and benchmarking functions that provides a library of surrogate models that is simple to use and facilitates the implementation of additional methods.
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Improving kriging surrogates of high-dimensional design models by Partial Least Squares dimension reduction
TL;DR: A covariance kernel is constructed that depends on only a few parameters and is constructed based on information obtained from the Partial Least Squares method, to replace computationally expensive codes with surrogate models.
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Efficient global optimization for high-dimensional constrained problems by using the Kriging models combined with the partial least squares method
TL;DR: The method first uses the ‘locating the regional extreme’ criterion, which incorporates minimizing the surrogate model while also maximizing the expected improvement criterion, and replaces the Kriging models by the KPLS(+K) models, which are more suitable for high-dimensional problems.
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Adaptive modeling strategy for constrained global optimization with application to aerodynamic wing design
Nathalie Bartoli,Thierry Lefebvre,Sylvain Dubreuil,Romain Olivanti,Rémy Priem,Nicolas Bons,Joaquim R. R. A. Martins,Joseph Morlier +7 more
TL;DR: A surrogate-based gradient-free optimization algorithm that can handle cases where the function evaluations are expensive, the computational budget is limited, the functions are multimodal, and the optimization problem includes nonlinear equality or inequality con- straints is developed.
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Surrogate modeling approximation using a mixture of experts based on EM joint estimation
TL;DR: An automatic method to combine several local surrogate models to build accurate and smooth approximation of discontinuous functions that are to be used in structural optimization problems and is found to improve the accuracy of the approximation.