research
∙
11/30/2022
Learning non-stationary and discontinuous functions using clustering, classification and Gaussian process modelling
Surrogate models have shown to be an extremely efficient aid in solving ...
share
research
∙
08/16/2022
Seismic fragility analysis using stochastic polynomial chaos expansions
Within the performance-based earthquake engineering (PBEE) framework, th...
research
∙
03/03/2022
Multi-objective robust optimization using adaptive surrogate models for problems with mixed continuous-categorical parameters
Explicitly accounting for uncertainties is paramount to the safety of en...
research
∙
02/07/2022
Stochastic polynomial chaos expansions to emulate stochastic simulators
In the context of uncertainty quantification, computational models are r...
research
∙
06/09/2021
Rare event estimation using stochastic spectral embedding
Estimating the probability of rare failure events is an essential step i...
research
∙
06/03/2021
A generalized framework for active learning reliability: survey and benchmark
Active learning methods have recently surged in the literature due to th...
research
∙
07/08/2020
A two-level Kriging-based approach with active learning for solving time-variant risk optimization problems
Several methods have been proposed in the literature to solve reliabilit...
research
∙
07/02/2020
Emulation of stochastic simulators using generalized lambda models
Computer simulations are used in virtually all fields of applied science...
research
∙
05/15/2020
Bayesian model inversion using stochastic spectral embedding
In this paper we propose a new sampling-free approach to solve Bayesian ...
research
∙
05/04/2020
Global sensitivity analysis for stochastic simulators based on generalized lambda surrogate models
Global sensitivity analysis aims at quantifying the impact of input vari...
research
∙
04/09/2020
Stochastic spectral embedding
Constructing approximations that can accurately mimic the behavior of co...
research
∙
11/20/2019
Replication-based emulation of the response distribution of stochastic simulators using generalized lambda distributions
Due to limited computational power, performing uncertainty quantificatio...
research
∙
09/16/2019
Bayesian calibration and sensitivity analysis of heat transfer models for fire insulation panels
A common approach to assess the performance of fire insulation panels is...
research
∙
09/16/2019
Heat transfer models for fire insulation panels: Bayesian calibration and sensitivity analysis
A common approach to assess the performance of fire insulation panels is...
research
∙
01/10/2019
Surrogate-assisted reliability-based design optimization: a survey and a new general framework
Reliability-based design optimization (RBDO) is an active field of resea...
research
∙
12/15/2018
Extending classical surrogate modelling to ultrahigh dimensional problems through supervised dimensionality reduction: a data-driven approach
Thanks to their versatility, ease of deployment and high-performance, su...
research
∙
10/22/2018
Surrogate modeling based on resampled polynomial chaos expansions
In surrogate modeling, polynomial chaos expansion (PCE) is popularly uti...
research
∙
08/09/2018
Data-driven polynomial chaos expansion for machine learning regression
We present a regression technique for data driven problems based on poly...
research
∙
06/13/2018
Development of probabilistic dam breach model using Bayesian inference
Dam breach models are commonly used to predict outflow hydrographs of po...
research
∙
09/27/2017
Gaussian process modelling using UQLab
We introduce the Gaussian process modelling module of the UQLab software...
research
∙
09/25/2017
A general framework for uncertainty quantification under non-Gaussian input dependencies
Uncertainty quantification (UQ) deals with the estimation of statistics ...
research
∙
09/22/2017
Hierarchical Kriging for multi-fidelity aero-servo-elastic simulators - Application to extreme loads on wind turbines
In the present work, we consider multi-fidelity surrogate modelling to f...
research
∙
02/13/2015
Polynomial-Chaos-based Kriging
Computer simulation has become the standard tool in many engineering fie...
research
∙
05/03/2011
Metamodel-based importance sampling for structural reliability analysis
Structural reliability methods aim at computing the probability of failu...
research
∙
04/19/2011
Reliability-based design optimization using kriging surrogates and subset simulation
The aim of the present paper is to develop a strategy for solving reliab...
research
∙
04/18/2011