Uncertainty and sensitivity analysis for long-running computer codes : a critical review

This thesis presents a critical review of existing methods for performing probabilistic uncertainty and sensitivity analysis for complex, computationally expensive simulation models. Uncertainty analysis (UA) methods reviewed include standard Monte Carlo simulation, Latin Hypercube sampling, importance sampling, line sampling, and subset simulation. Sensitivity analysis (SA) methods include scatter plots, Monte Carlo filtering, regression analysis, variance-based methods (Sobol' sensitivity indices and Sobol' Monte Carlo algorithms), and Fourier amplitude sensitivity tests. In addition, this thesis reviews several existing metamodeling techniques that are intended provide quick-running approximations to the computer models being studied. Because stochastic simulation-based UA and SA rely on a large number (e.g., several thousands) of simulations, metamodels are recognized as a necessary compromise when UA and SA must be performed with long-running (i.e., several hours or days per simulation) computational models. This thesis discusses the use of polynomial Response Surfaces (RS), Artificial Neural Networks (ANN), and Kriging/Gaussian Processes (GP) for metamodeling. Moreover, two methods are discussed for estimating the uncertainty introduced by the metamodel. The first of these methods is based on a bootstrap sampling procedure, and can be utilized for any metamodeling technique.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Nuclear Science and Engineering.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2010

Author dc:creator

• Langewisch, Dustin R

Advisor dc:contributor.advisor

• George E. Apostolakis.

• Nuclear Science and Engineering.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/58285

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/58285