Uncertainty Quantification of Stochastic Simulation for Black-box Computer Experiments

Stochastic simulations applied to black-box computer experiments are becoming more widely used to evaluate the reliability of systems. Yet, the reliability evaluation or computer experiments involving many replications of simulations can take significant computational resources as simulators become...

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Bibliographic Details
Published in:Methodology and computing in applied probability Vol. 20; no. 4; pp. 1155 - 1172
Main Authors: Choe, Youngjun, Lam, Henry, Byon, Eunshin
Format: Journal Article
Language:English
Published: New York Springer US 01.12.2018
Springer Nature B.V
Subjects:
Business and Management
Central limit theorem
Computer simulation
Confidence intervals
Economics
Electrical Engineering
Experiments
Importance sampling
Life Sciences
Mathematics and Statistics
Probability theory
Reliability analysis
Reliability engineering
Simulators
Statistical analysis
Statistics
Structural reliability
System reliability
Systems analysis
Uncertainty
Wind turbines
68U20
Central limit theorem
Variance reduction
65C05
62F12
Monte Carlo simulation
Confidence interval
Importance sampling
ISSN:1387-5841, 1573-7713
Online Access:Get full text
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Summary:Stochastic simulations applied to black-box computer experiments are becoming more widely used to evaluate the reliability of systems. Yet, the reliability evaluation or computer experiments involving many replications of simulations can take significant computational resources as simulators become more realistic. To speed up, importance sampling coupled with near-optimal sampling allocation for these experiments is recently proposed to efficiently estimate the probability associated with the stochastic system output. In this study, we establish the central limit theorem for the probability estimator from such procedure and construct an asymptotically valid confidence interval to quantify estimation uncertainty. We apply the proposed approach to a numerical example and present a case study for evaluating the structural reliability of a wind turbine.
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ISSN:1387-5841
1573-7713
DOI:10.1007/s11009-017-9599-7