IPSAL: Implementation of the module to generate the Sobol sequence and indices
Sensitivity and uncertainty analysis hold significant importance across a range of applications, spanning from industrial problems to climate change, financial risk assessment, as well as mathematical and computational models. These analyses involve identifying influential input parameters in models...
| Authors: | , , , , , |
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| Format: | article |
| Status: | Published version |
| Publication Date: | 2023 |
| Country: | Brasil |
| Institution: | Universidade Federal do Rio Grande (FURG) |
| Repository: | Vetor (Online) |
| Language: | English |
| OAI Identifier: | oai:ojs.periodicos.furg.br:article/16439 |
| Online Access: | https://periodicos.furg.br/vetor/article/view/16439 |
| Access Level: | Open access |
| Keyword: | sensitivity analysis sobol method unusual operation parameter correlation computational modeling applied mathematics inverse problem análise de sensibilidade Método de Sobol operação não usual correlação de parâmetros |
| Summary: | Sensitivity and uncertainty analysis hold significant importance across a range of applications, spanning from industrial problems to climate change, financial risk assessment, as well as mathematical and computational models. These analyses involve identifying influential input parameters in models to comprehend their impact on the output. Sensitivity analysis can be performed locally, examining parameter effects at a fixed value, or globally, evaluating the model across a range of parameter values. The Sobol method stands as a robust approach for global sensitivity analysis, employing a Sobol sequence to create samples more uniformly within the input parameter space, thus enabling efficient exploration of model inputs. This paper aims to introduce a computational implementation in Scilab to generate the Sobol sequence for utilization in sensitivity analysis through the Sobol method. A test case was applied to generate Sobol sequences and discuss the obtained results. |
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