.png){kind=icon}
.png){kind=icon}
Return
A new sparse grid based method for uncertainty propagation
.png){kind=icon}
PRE
AI
DOI：10.1007/s00158-009-0441-x.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Current methods for uncertainty propagation suffer from their limitations in providing accurate and efficient solutions to high-dimension problems with interactions of random variables. The sparse grid technique, originally invented for numerical integration and interpolation, is extended to uncertainty propagation in this work to overcome the difficulty. The concept of Sparse Grid Numerical Integration (SGNI) is extended for estimating the first two moments of performance in robust design, while the Sparse Grid Interpolation (SGI) is employed to determine failure probability by interpolating the limit-state function at the Most Probable Point (MPP) in reliability analysis. The proposed methods are demonstrated by high-dimension mathematical examples with notable variate interactions and one multidisciplinary rocket design problem. Results show that the use of sparse grid methods works better than popular counterparts. Furthermore, the automatic sampling, special interpolation process, and dimension-adaptivity feature make SGI more flexible and efficient than using the uniform sample based metamodeling techniques.
Keywords:
Uncertainty propagation
Sparse grid
Most probable point
High dimension
Variate interaction
Robust design
Reliability analysis
Journal
IF:
4
Papers:
4.8K
Citations:
1.7W
Organization
View more organizations