Gradient-enhanced universal kriging with polynomial chaos as trend function

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

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


In this paper, we propose a new formulation of gradient-enhanced universal Kriging that uses sparse polynomial chaos expansion (PCE) as trend functions. In this regard, the gradient information is used to improve both the trend function and the Gaussian process part. The optimal set of polynomial terms is selected based on the least angle regression algorithm. We tested the performance of the proposed gradient-enhanced polynomial chaos Kriging (GEPCK) in several algebraic and non-algebraic test cases and compared it with ordinary Kriging (OK) and ordinary gradient-enhanced Kriging (GEK). Results show that GEPCK consistently outperformed other methods or at least competitive to the best performing method on both algebraic and non-algebraic problems. This indicates that the performance of the conventional GEK can be further improved by incorporating sparse PCE as trend functions.

Original languageEnglish
Title of host publicationAIAA Scitech 2020 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624105951
Publication statusPublished - 2020
Externally publishedYes
EventAIAA Scitech Forum, 2020 - Orlando, United States
Duration: 2020 Jan 62020 Jan 10

Publication series

NameAIAA Scitech 2020 Forum
Volume1 PartF


ConferenceAIAA Scitech Forum, 2020
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Aerospace Engineering


Dive into the research topics of 'Gradient-enhanced universal kriging with polynomial chaos as trend function'. Together they form a unique fingerprint.

Cite this