Surrogate based runtime difference mitigation in asynchronous multi-disciplinary search tasks

Bayesian approaches to the optimisation of complex systems have attracted much research in recent years and have achieved encouraging success.

The project has mainly two aims:

1. Develop new training algorithms and new optimisation methods that can deal with very low amount of training data for surrogate models and optimisation evaluations.
2. Develop new infill criteria for Bayesian approaches to optimisation which integrate multiple models for estimating different criteria of a multi-objective problem or constraints.