Présentation sur YouTube.

In the field of Multi-fidelity Expensive Black-Box (Mf-EBB), a problem instance consists of an expensive yet accurate source of information, and one or more cheap yet less accurate sources of information. The field aims to provide techniques either to accurately explain how decisions affect design outcome, or to find the best decisions to optimise design outcomes. Many techniques which use surrogate models have been developed to provide solutions to both aims. Only in recent years, however, have researchers begun to explore the conditions under which these new techniques are reliable, often focusing on problems with a single low-fidelity function, known as Bi-fidelity Expensive Black-Box (Bf-EBB) problems.

In this talk I will present an introduction to Bf-EBB problems. I will give the formulation of three variations of Bf-EBB problems and illustrate them with a toy problem. I will also discuss the question of how much techniques should rely on the low fidelity source, if at all, when approaching these problems.