Optimization Employing Gaussian Process-Based Surrogates

The optimization of complex plasma-wall interaction and material science models is tantamount with long-running and expensive computer simulations. This indicates the use of surrogate-based methods in the optimization process. A Gaussian process (GP)-based Bayesian adaptive exploration method has been developed and validated on mock examples. The self-consistent adjustment of hyperparameters according to the information present in the data turns out to be the main benefit from the Bayesian approach. While the overall properties and performance is favorable (in terms of expensive function evaluations), the optimal balance between local and global exploitation still mandates further research for strongly multimodal optimization problems.