Gaussian processes and support vector regression for uncertainty quantification in aerodynamics

Pramudita Satria Palar, Kemas Zakaria, Lavi Rizki Zuhal, Koji Shimoyama, Rhea Patricia Liem

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper investigates the performance of two kernel-based surrogate models, namely Gaussian Process regression (GPR) and support vector regression (SVR), for solving uncertainty quantification (UQ) problems in aerodynamics. This research aims to shed light on both surrogate models’ approximation performance to get better insight for practical purposes. To that end, experiments using various kernel functions were performed to study their impact on GPR and SVR accuracy. Besides, the use of a composite kernel learning technique is also studied. Computational experiments show that GPR with Matern-5/2 is the most robust technique when an individual kernel is used. However, SVR with the Matern-5/2 kernel also performs better than GPR in some problems. The results suggest that there is no single best performing method when averaged over all sets of problems. Finally, we also demonstrated that using composite kernel learning, provided sufficient data samples, can further reduce the approximation error for both GPR and SVR.

Original languageEnglish
Title of host publicationAIAA Scitech 2021 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
Pages1-12
Number of pages12
ISBN (Print)9781624106095
Publication statusPublished - 2021
EventAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2021 - Virtual, Online
Duration: 2021 Jan 112021 Jan 15

Publication series

NameAIAA Scitech 2021 Forum

Conference

ConferenceAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2021
CityVirtual, Online
Period21/1/1121/1/15

ASJC Scopus subject areas

  • Aerospace Engineering

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