Vincent Dubourg

TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.

TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.

TL;DR: In this paper, the authors propose to use a Kriging surrogate for the performance function as a means to build a quasi-optimal importance sampling density, which can be applied to analytical and finite element reliability problems and proves efficient up to 100 basic random variables.

TL;DR: The aim of the present paper is to develop a strategy for solving reliability-based design optimization (RBDO) problems that remains applicable when the performance models are expensive to evaluate.

TL;DR: This manuscript proposes a surrogate-based strategy where the limit-state function is progressively replaced by a Kriging meta-model, a probabilistic design approach aimed at considering the uncertainty attached to the system of interest in order to provide optimal and safe solutions.