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Activity Number: 28 - Computation, Design, and Quality Assurance of Physical Science and Engineering Applications
Type: Contributed
Date/Time: Sunday, August 8, 2021 : 1:30 PM to 3:20 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract #318879
Title: Learning and Deploying Active Subspaces on Black Box Simulators
Author(s): Nathan Wycoff* and Mickaƫl Binois and Stefan M Wild and Robert B Gramacy
Companies: Virginia Tech and INRIA and argonne national laboratory and Virginia Tech
Keywords: Computer Experiment; Nonparametrics; Sensitivity Analysis; Multivariate Analysis; Dimension Reduction

Surrogate modeling of computational simulations 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 discuss the relationship between several sensitivity analysis methods based on kernel regression before describing how they give rise to a transformation of the input variables. Specifically, we perform an input warping such that the "warped simulator" is equally sensitive to all input directions, freeing local models to focus on local dynamics. Numerical experiments on observational data and benchmark test functions, including a high-dimensional computer simulator from the automotive industry, provide empirical validation.

Authors who are presenting talks have a * after their name.

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