Efficient Adaptive Design of Experiments for Gradient-Enhanced Gaussian Process Surrogates in Inverse Problems
A fully adaptive greedy approach to the computational design of experiments problem using gradient-enhanced Gaussian process regression as surrogates. The approach optimizes both the choice of evaluation points and the required simulation accuracy, for both values and gradients of the forward model, to efficiently construct accurate surrogate models for inverse problems.