Paper Info
Title
Finite dimensional models for random functions.
Abstract
Truncated Karhunen–Loève (KL) representations are used to construct finite dimensional (FD) models for non-Gaussian functions with finite variances. The second moment specification of the random coefficients of these representations are enhanced to full probabilistic characterization by using translation, polynomial chaos, and translated polynomial chaos models, referred to as T, PC, and PCT models. Following theoretical considerations on KL representations and T, PC, and PCT models, three numerical examples are presented to illustrate the implementation and performance of these models. The PCT models inherit the desirable features of both T and PC models. It approximates accurately all quantities of interest considered in these examples.
Year
DOI
Venue
2019
10.1016/j.jcp.2018.09.029
Journal of Computational Physics
Keywords
Field
DocType
Convergence of random series,Karhunen–Loève series,Optimization,Polynomial chaos,Translation vectors
Applied mathematics,Mathematical analysis,Polynomial chaos,Probabilistic logic,Mathematics,Second moment of area
Journal
Volume
ISSN
Citations 
376
0021-9991
0
PageRank 
References 
Authors
0.34
8
1
Name
Order
Citations
PageRank
Mircea Grigoriu143.83