Latin hypercube sampling for stochastic finite element analysis
(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:
https://lup.lub.lu.se/record/344421
- author
- Olsson, Anders LU and Sandberg, Göran LU
- organization
- publishing date
- 2002
- 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
- 2016-04-01 12:20:03
- date last changed
- 2022-04-21 05:59:43
@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}}, keywords = {{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)}}, doi = {{10.1061/(ASCE)0733-9399(2002)128:1(121)}}, volume = {{128}}, year = {{2002}}, }