Articlediamond2022

GlobalSensitivity.jl: Performant and Parallel Global Sensitivity Analysis with Julia

48 citations0/0 files storedThe Journal of Open Source Software

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Probabilistic and Robust Engineering Design Simulation Techniques and Applications Gaussian Processes and Bayesian Inference

Abstract

Global Sensitivity Analysis (GSA) methods are used to quantify the uncertainty in the output of a model with respect to the parameters.These methods allow practitioners to measure both parameters' individual contributions and the contribution of their interactions to the output uncertainty.GlobalSensitivity.jl is a Julia (Bezanson et al., 2017) package containing implementations of some of the most popular GSA methods.Currently it supports Delta Moment-Independent (Borgonovo, 2007;

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2007Global Sensitivity Analysis. The Primer 2001Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates 1991Factorial Sampling Plans for Preliminary Computational Experiments 1999A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output 2002Making best use of model evaluations to compute sensitivity indices 2007An effective screening design for sensitivity analysis of large models 2017SALib: An open-source Python library for Sensitivity Analysis 2006Ten iterative steps in development and evaluation of environmental models

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