34 citations · 112 across the 10 of their papers we have counts for
11 papers
Active learning strategy for excursion-set confidence regions of functional simulator outputs
Lucas Brunel, Mathieu Balesdent, Loïc Brevault +2
Estimating excursion set confidence regions seeks to identify regions where a function may exceed some threshold with a given confidence level. This paper focuses on estimating suc…
Augmented Quantization: Mixture Models for Risk-Oriented Sensitivity Analysis
Charlie Sire, Didier Rullière, Rodolphe Le Riche +3
A central question in risk analysis is to identify the factors that drive the system toward a specific hazardous outcome, such as the exceedance of a given threshold. When relying…
FunQuant: A R package to perform quantization in the context of rare events and time-consuming simulations
Charlie Sire, Yann Richet, Rodolphe Le Riche +3
Quantization summarizes continuous distributions by calculating a discrete approximation. Among the widely adopted methods for data quantization is Lloyd's algorithm, which partiti…
Quantizing rare random maps: application to flooding visualization
Charlie Sire, Rodolphe Le Riche, Didier Rullière +3
Visualization is an essential operation when assessing the risk of rare events such as coastal or river floodings. The goal is to display a few prototype events that best represent…
Coupling and selecting constraints in Bayesian optimization under uncertainties
Julien Pelamatti, Rodolphe Le Riche, Céline Helbert +1
We consider chance constrained optimization where it is sought to optimize a function while complying with constraints, both of which are affected by uncertainties. The high comput…
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…
Revisiting Bayesian Optimization in the light of the COCO benchmark
Rodolphe Le Riche, Victor Picheny
It is commonly believed that Bayesian optimization (BO) algorithms are highly efficient for optimizing numerically costly functions. However, BO is not often compared to widely dif…
A sampling criterion for constrained Bayesian optimization with uncertainties
Reda El Amri, Rodolphe Le Riche, Céline Helbert +2
We consider the problem of chance constrained optimization where it is sought to optimize a function and satisfy constraints, both of which are affected by uncertainties. The real…
Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version
David Gaudrie, Rodolphe Le Riche, Victor Picheny +2
Parametric shape optimization aims at minimizing an objective function f(x) where x are CAD parameters. This task is difficult when f is the output of an expensive-to-evaluate nume…
Targeting Solutions in Bayesian Multi-Objective Optimization: Sequential and Batch Versions
David Gaudrie, Rodolphe Le Riche, Victor Picheny +2
Multi-objective optimization aims at finding trade-off solutions to conflicting objectives. These constitute the Pareto optimal set. In the context of expensive-to-evaluate functio…
Identification of Heterogeneous Elastic Properties in Stenosed Arteries: a Numerical Plane Strain Study
Alexandre Franquet, Stéphane Avril, Pierre Badel +1
Assessing the vulnerability of atherosclerotic plaques requires an accurate knowledge of the mechanical properties of the plaque constituents. It is possible to measure displacemen…