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Latin hypercube sampling for stochastic finite element analysis

Olsson, Anders LU and Sandberg, Göran LU (2002) In Journal of Engineering Mechanics 128(1). p.121-125
Abstract
A Latin hypercube sampling method, including a reduction of spurious correlation in input data, is suggested for stochastic finite element analysis. This sampling procedure strongly improves the representation of stochastic design parameters compared to a standard Monte Carlo sampling. As the correlation control requires the number of realizations to be larger than the number of stochastic variables in the problem, a principal component analysis is employed to reduce the number of stochastic variables. In many cases, this considerably relaxes the restriction on the number of realizations. The method presented offers the same general applicability as the standard Monte Carlo sampling method but is superior in computational efficiency.
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
stochastic processes, finite element method, sampling design
in
Journal of Engineering Mechanics
volume
128
issue
1
pages
121 - 125
publisher
American Society of Civil Engineers (ASCE)
external identifiers
  • wos:000173367000014
  • scopus:0036172940
ISSN
1943-7889
DOI
10.1061/(ASCE)0733-9399(2002)128:1(121)
language
English
LU publication?
yes
id
9196285a-43f2-470a-9fcf-4b1a9948ebdb (old id 344421)
date added to LUP
2007-11-08 08:20:54
date last changed
2017-12-10 03:51:38
@article{9196285a-43f2-470a-9fcf-4b1a9948ebdb,
  abstract     = {A Latin hypercube sampling method, including a reduction of spurious correlation in input data, is suggested for stochastic finite element analysis. This sampling procedure strongly improves the representation of stochastic design parameters compared to a standard Monte Carlo sampling. As the correlation control requires the number of realizations to be larger than the number of stochastic variables in the problem, a principal component analysis is employed to reduce the number of stochastic variables. In many cases, this considerably relaxes the restriction on the number of realizations. The method presented offers the same general applicability as the standard Monte Carlo sampling method but is superior in computational efficiency.},
  author       = {Olsson, Anders and Sandberg, Göran},
  issn         = {1943-7889},
  keyword      = {stochastic processes,finite element method,sampling design},
  language     = {eng},
  number       = {1},
  pages        = {121--125},
  publisher    = {American Society of Civil Engineers (ASCE)},
  series       = {Journal of Engineering Mechanics},
  title        = {Latin hypercube sampling for stochastic finite element analysis},
  url          = {http://dx.doi.org/10.1061/(ASCE)0733-9399(2002)128:1(121)},
  volume       = {128},
  year         = {2002},
}