hal-02986558
https://ifp.hal.science/hal-02986558
https://ifp.hal.science/hal-02986558v2/document
https://ifp.hal.science/hal-02986558v2/file/DreamTeam_jointInversion_soumis.pdf
Set inversion under functional uncertainties with Gaussian ProcessRegression defined in the joint space of control and uncertain
El Amri, Reda
Helbert, Céline
Munoz Zuniga, Miguel
Prieur, Clémentine
Sinoquet, Delphine
[MATH.MATH-ST] Mathematics [math]/Statistics [math.ST]
[MATH] Mathematics [math]
[MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC]
UNDEFINED
Functional uncertainties
Data reduction
Gaussian Process models
Set inversion
In this paper we propose an efficient sampling strategy to solve an inversion problem subjected to functional uncertainties. More precisely, we aim at characterizing a control variable region defined by exceedance above a prescribed threshold of specific Quantities of Interest (QoT). This study is motivated by an automotive industrial application consisting in the identification of the set of values of control variables of a gas after-treatment device, in line with pollutant emission standards of a vehicle under driving profile uncertainties. In that context, driving profile uncertainties are modelled by a functional random variable and the constrained response in the inversion problem is formulated as the expectation over this functional random variable only known through a set of realizations. As often in industrial applications, this problem involves time-consuming computational models. We thus propose an approach that uses Gaussian Process meta-models built on the joint space of control and uncertain input variables. Specifically, we define a learning criterion based on uncertainty in the excursion of the Gaussian Process and derive tractable expressions for variance reduction in such a framework. Applications to analytical examples, followed by the automotive industrial test case show the accuracy and the efficiency brought by the procedure we propose.
2021
2021-09-30
en