Versatile surrogate-based optimization of medium-scale and large-scale problems.

The main objective of this project is the development of versatile methods based on the Gaussian Process surrogate model to expedite engineering tasks for a wide range of medium-scale (20-100 dimensions) and large-scale (>100 dimensions) problems. The aim is to create a framework of GP-based methods that is easy-to-use (“one-button” approach) and adapts itself to the problem at hand (“self-tuning”) to solve it as efficiently as possible.