J
Jon C. Helton
Researcher at Arizona State University
Publications - 160
Citations - 12863
Jon C. Helton is an academic researcher from Arizona State University. The author has contributed to research in topics: Waste Isolation Pilot Plant & Uncertainty analysis. The author has an hindex of 43, co-authored 160 publications receiving 11926 citations. Previous affiliations of Jon C. Helton include Sandia National Laboratories.
Papers
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Journal ArticleDOI
Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems
Jon C. Helton,Freddie J. Davis +1 more
TL;DR: The following techniques for uncertainty and sensitivity analysis are briefly summarized: Monte Carlo analysis, differential analysis, response surface methodology, Fourier amplitude sensitivity test, Sobol' variance decomposition, and fast probability integration.
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Survey of sampling-based methods for uncertainty and sensitivity analysis
TL;DR: Sampling-based methods for uncertainty and sensitivity analysis are reviewed and special attention is given to the determination of sensitivity analysis results.
Journal ArticleDOI
Uncertainty and sensitivity analysis techniques for use in performance assessment for radioactive waste disposal
TL;DR: In this article, a review of uncertainty and sensitivity analysis techniques for use in performance assessments for radioactive waste disposal is presented. But, the most widely used technique for performance assessment is Monte Carlo analysis.
ReportDOI
Latin Hypercube Sampling and the Propagation of Uncertainty in Analyses of Complex Systems
Jon C. Helton,Freddie J. Davis +1 more
TL;DR: The following techniques for uncertainty and sensitivity analysis are briefly summarized: Monte Carlo analysis, differential analysis, response surface methodology, Fourier amplitude sensitivity test, Sobol’ variance decomposition, and fast probability integration.
Journal ArticleDOI
Challenge problems: uncertainty in system response given uncertain parameters
TL;DR: This paper describes the challenge problems and gives numerical values for the different input parameters so that results from different investigators can be directly compared and develop a better understanding of the relative advantages and disadvantages of traditional and newer methods.