Outlier-Robust Surrogate Modelling of Ion-Solid Interaction Simulations

Abstract

Data for complex plasma–wall interactions require long-running and expensive computer simulations of codes like EIRENE or SOLPS. Furthermore, the number of input parameters is large, which results in a low coverage of the (physical) parameter space. Unpredictable occasions of outliers create a need to conduct the exploration of this multi-dimensional space using robust analysis tools. We restate the Gaussian-process (GP) method as a Bayesian adaptive exploration method for establishing surrogate surfaces in the variables of interest. On this basis, we complete the analysis by the Student-t process (TP) method in order to improve the robustness of the result with respect to outliers. The most obvious difference between both methods shows up in the marginal likelihood for the hyperparameters of the covariance function where the TP method features a broader marginal probability distribution in the presence of outliers.