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Gaussian processes and support vector regression for uncertainty quantification in aerodynamics
Palar P.S.a, Zakaria K.a, Zuhal L.R.a, Shimoyama K.b, Liem R.P.c
a Institut Teknologi Bandung, West Java, Indonesia
b Institute of Fluid Science, Tohoku University, Sendai, 980-8577, Japan
c The Hong Kong University of Science and Technology, Hong Kong
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2021, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.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.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]Pramudita Satria Palar and Lavi Rizki Zuhal were funded in part through the Penelitian Dasar administered by Direktorat Riset dan Pengabdian Masyarakat-Direktor Jenderal Penguatan Riset dan Pengembangan-Kementerian Riset dan Teknologi / Badan Riset dan Inovasi nasional, Republik Indonesia. Part of the work was also carried out under the Collaborative Research Project 2020 of the Institute of Fluid Science, Tohoku University.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]