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Activity Number: 354 - Experimental Design and Reliability
Type: Contributed
Date/Time: Thursday, August 12, 2021 : 10:00 AM to 11:50 AM
Sponsor: Section on Physical and Engineering Sciences
Abstract #318386
Title: Gaussian Process structure for the emulation of deterministic and stochastic solvers: a simulation study
Author(s): Luca Pegoraro* and Luigi Salmaso and Riccardo Ceccato and Rosa Arboretti
Companies: University of Padova and University of Padova and University of Padova and University of Padova
Keywords: Gaussian Process; Simulation study; Kernel; Homoscedastic; Heteroscedastic

The increased diffusion of complex numerical solvers to emulate physical processes demands the development of fast and accurate surrogate models. Gaussian Processes (GPs) are the most widely adopted models in this context, as they proved to be sufficiently flexible to effectively mimic the behaviour of complex phenomena and they also provide a quantification of uncertainty of predictions. However, the accuracy of the model depends on both the trend component and covariance structure. In this work we conduct an extensive simulation study that investigates the performance of several GP structures considering the deterministic, homoscedastic and heteroscedastic noise settings. As a result, the findings of this work provide guidelines to practitioners dealing with both deterministic and stochastic solvers.

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

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