An enhanced data-driven polynomial chaos method for uncertainty propagation

ABSTRACT

As a novel type of polynomial chaos expansion (PCE), the data-driven PCE (DD-PCE) approach has been developed to have a wide range of potential applications for uncertainty propagation. While the research on DD-PCE is still ongoing, its merits compared with the existing PCE approaches have yet to be understood and explored, and its limitations also need to be addressed. In this article, the Galerkin projection technique in conjunction with the moment-matching equations is employed in DD-PCE for higher-dimensional uncertainty propagation. The enhanced DD-PCE method is then compared with current PCE methods to fully investigate its relative merits through four numerical examples considering different cases of information for random inputs. It is found that the proposed method could improve the accuracy, or in some cases leads to comparable results, demonstrating its effectiveness and advantages. Its application in dealing with a Mars entry trajectory optimization problem further verifies its effectiveness.

KEYWORDS:

• Uncertainty propagation
• polynomial chaos
• data-driven
• Galerkin projection
• trajectory optimization

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The grant supports from National Natural Science Foundation of China [No. 51105034] is greatly acknowledged.