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Activity Number: 199 - Innovations in Modeling Computer Experiments
Type: Contributed
Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract #313357
Title: Statistical Emulation for High-Dimensional Complex Simulators
Author(s): Gang Yang* and Emily Kang and Bledar Konomi
Companies: University of Cincinnati and University of Cincinnati and University of Cincinnati
Keywords: Dimension reduction; Functional principal component analysis; Gaussian process; Statistical emulation
Abstract:

The Gaussian process emulator is a frequently used surrogate for computationally expensive simulators to quantify uncertainty and improve process understanding. We propose a joint framework for constructing low-dimensional approximations of complex simulators by combining Gaussian process emulation technique with dimension reductions for both input and output spaces in a data-driven way: Functional principal component analysis (FPCA) procedure via a conditional expectation method which provides the best linear prediction of functional principal component scores is incorporated to reduce dimension of output space. The gradient-based kernel dimension reduction method is applied to reduce the dimension of input space when the gradients of the complex simulator is unavailable or computationally prohibitive. Theoretical properties of the resulting statistical emulator are explored, and the performance of our approach is illustrated with numerical studies.


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

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