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Variance-based sensitivity analysis (often referred to as the Sobol’ method or Sobol’ indices, after Ilya M. Sobol’) is a form of global sensitivity analysis. Working within a probabilistic framework, it decomposes the variance of the output of the model or system into fractions which can be attributed to inputs or sets of inputs.
20 de jun. de 2022 · The Sobol Indices. Intuition. The importance of an input variable X_i is measured by the part of the variance of Y for which it is responsible, that is, if we fix X_i, we look at how much the variance (of Y) has decreased.
26 de feb. de 2015 · The Sobol sensitivity analysis is divided into four steps: generating parameter sets, running and simulation the model output with the generated parameter sets, calculating, and analyzing the total-, first-, and second-order and higher-order Sobol sensitivity indices.
Se presenta una técnica numéricamente eficiente para estimar los índices de Sobol' para una clase particular de sistemas estructurales. La técnica propuesta se basa en los conceptos de ...
17 de jun. de 2017 · This section aims at presenting an overview of variance-based approaches for global sensitivity analysis. Starting from functional ANOVA, Sobol’ indices are first defined and then estimation algorithms are provided. The performance of these algorithms is theorically and practically discussed.
Learn how to use sparse PCE to calculate higher-order Sobol' indices with 100 input parameters. Learn how to obtain the Sobol' indices using either the sampling-based or the PCE/LRA-based methods.
The Sobol indices are a tool for explaining the variance of the output of a function as components of the input variables. Monte Carlo is an approach for computing these indices if the function is cheap to evaluate.
