Due to the expensive cost of simulations, the total number of model runs is often limited. This motivates the use of surrogates which help reduce the computational cost, and can be used to choose the next point to evaluate.
In this presentation, we will go over the principles of Bayesian Optimization for single and multi objective problems, possibly constrained, and how it can be adapted when uncertainties are introduced in the modelling.