Nathan Wycoff, Georgetown University

• Title: Learning And Deploying Active Subspaces On Black Box Simulators

• Abstract: Surrogate modeling of computer experiments via local models, which induce sparsity by only considering short range interactions, can tackle huge analyses of complicated input-output relationships. However, narrowing focus to local scale means that global trends must be relearned over and over again. We first demonstrate how to use Gaussian processes to efficiently perform a global sensitivity analysis on an expensive black box simulator. We next propose a framework for incorporating information from this global sensitivity analysis into the surrogate model as an input rotation and rescaling preprocessing step. We further discuss applications to derivative free optimization via locally defined subspaces. Numerical experiments on observational data and benchmark test functions provide empirical validation.