18 citations · 22 across the 6 of their papers we have counts for
7 papers
A comparison of mixed-variables Bayesian optimization approaches
Jhouben Cuesta-Ramirez, Rodolphe Le Riche, Olivier Roustant +3
Most real optimization problems are defined over a mixed search space where the variables are both discrete and continuous. In engineering applications, the objective function is t…
Bootstrapping time correlation functions of molecular dynamics
Desbiens N., Arnault P., Weens W. +2
Molecular dynamics is often considered as a numerical experiment. The error bars on the results are therefore mandatory, but sometimes difficult to determine and computationally de…
Active learning of tree tensor networks using optimal least-squares
Cécile Haberstich, Anthony Nouy, Guillaume Perrin
In this paper, we propose new learning algorithms for approximating high-dimensional functions using tree tensor networks in a least-squares setting. Given a dimension tree or arch…
On the quantification of discretization uncertainty: comparison of two paradigms
Julien Bect, Souleymane Zio, Guillaume Perrin +2
Numerical models based on partial differential equations (PDE), or integro-differential equations, are ubiquitous in engineering and science, making it possible to understand or de…
Boosted optimal weighted least-squares
Cécile Haberstich, Anthony Nouy, Guillaume Perrin
This paper is concerned with the approximation of a function in a given approximation space of dimension from evaluations of the function at suitably chosen point…
Group kernels for Gaussian process metamodels with categorical inputs
Olivier Roustant, Esperan Padonou, Yves Deville +4
Gaussian processes (GP) are widely used as a metamodel for emulating time-consuming computer codes. We focus on problems involving categorical inputs, with a potentially large numb…