Augmented surrogates for predictive extrapolation in electromagnetics

Surrogate models are a popular solution for predictive science of complex models with restricted computational budgets. We introduce the augmented surrogate, an adaption allowing both extrapolation and cost reduction for training the surrogate, and give an illustrative example for a prediction scenario for the Helmholtz problem.