Applications of Gaussian Process Regression in the Aero-Thermo-Servo-Elastic Analysis Towards Integrated Hypersonic Flight Dynamic Analysis

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

1 Scopus citations

Abstract

This tutorial paper presents the surrogate modeling based on Gaussian process regression (GPR) and its application in the hypersonic aero-thermo-servo-elastic analysis, a key ingredient in the design of hypersonic vehicles as well as its guidance, navigation and control. First, the basic formulations of GPR and practices of model training are presented. Next, the existing applications of GPR in hypersonic problems are reviewed. Subsequently, a pedagogical example and an applied example of GPR are presented. Finally, the pros and cons of the GPR modeling approach for general and hypersonic-specific applications are summarized.

Original languageEnglish (US)
Title of host publication60th IEEE Conference on Decision and Control, CDC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6-15
Number of pages10
ISBN (Electronic)9781665436595
DOIs
StatePublished - 2021
Event60th IEEE Conference on Decision and Control, CDC 2021 - Austin, United States
Duration: Dec 13 2021Dec 17 2021

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume2021-December
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370

Conference

Conference60th IEEE Conference on Decision and Control, CDC 2021
Country/TerritoryUnited States
CityAustin
Period12/13/2112/17/21

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

Fingerprint

Dive into the research topics of 'Applications of Gaussian Process Regression in the Aero-Thermo-Servo-Elastic Analysis Towards Integrated Hypersonic Flight Dynamic Analysis'. Together they form a unique fingerprint.

Cite this