  We propose a novel method for gradient-based optimization of black-box
simulators using differentiable local surrogate models. In fields such as
physics and engineering, many processes are modeled with non-differentiable
simulators with intractable likelihoods. Optimization of these forward models
is particularly challenging, especially when the simulator is stochastic. To
address such cases, we introduce the use of deep generative models to
iteratively approximate the simulator in local neighborhoods of the parameter
space. We demonstrate that these local surrogates can be used to approximate
the gradient of the simulator, and thus enable gradient-based optimization of
simulator parameters. In cases where the dependence of the simulator on the
parameter space is constrained to a low dimensional submanifold, we observe
that our method attains minima faster than baseline methods, including Bayesian
optimization, numerical optimization, and approaches using score function
gradient estimators.
