David Ginsbourger (University of Bern)
Gaussian random field models for the adaptive design of costly experiments
Abstract:
Gaussian random field models have become commonplace in the design and analysis of costly experiments.
Thanks to convenient properties of associated conditional distributions(Gaussianity, interpolation in the case of deterministic responses,etc.), Gaussian random field models not only allow predicting black-box responses for untried input configurations, but can also be used as a basis for evaluation strategies dedicated to optimization, inversion, uncertainty quantification, probability of failure estimation, and more.
After an introduction to Gaussian random field modelling and some of its popular applications in adaptive design of deterministic numerical experiments, we will present two recent contributions.First, results on infill sampling criteria for uncertainty reduction will be presented and illustrated, with application to an excursion set estimation problem from safety engineering.
Second, we will focus on a high-dimensional application of Gaussian field modelling to an inversion problem in water sciences, where an original non-stationary covariance kernel relying on fast proxy simulations is used.
**This is a joint seminar between the Statistics & Probability Group and the Scientific Computation Group.**