Exploiting Gradients in Bayesian Inference of Expensive Simulators
The paper introduces gradient information into Bayesian Optimization for Simulator Inverse Problems (BOSIP) by using a gradient-enhanced Gaussian Process surrogate model. This method accelerates inference for expensive simulators by providing guidance during active learning, significantly improving posterior convergence and approximation quality, especially when reverse-mode differentiation is computationally efficient.