Orateur
Christophette Blanchet-Scalliet
(École Centrale de Lyon)
Description
The goal of this work is to use goal-directed sensitivity analysis in order to
reduce the cost of solving a robust optimization problem. Specifically, we focus on
quantifying the impact of uncertain inputs on feasible sets, which are subsets of the
design domain. While most sensitivity analysis methods deal with scalar outputs,
we introduce a novel approach for performing sensitivity analysis with setvalued
outputs. We propose a kernel designed for set-valued outputs and use the Hilbert-Schmidt Independence Criterion (HSIC) . The proposed methodology is implemented to carry out an uncertainty analysis for time-averaged concentration maps of pollutants using a Gaussian Processs Regression as an emulator.
