Abstract
We provide a sensitivity analysis toolbox consisting of a set of Matlab functions that offer utilities for quantifying the influence of uncertain input parameters on uncertain model outputs. It allows the determination of the key input parameters of an output of interest. The results are based on a probability density function (PDF) provided for the input parameters. The toolbox for uncertainty and sensitivity analysis methods consists of three ingredients: (1) sampling method, (2) surrogate models, (3) sensitivity analysis (SA) method. Numerical studies based on analytical functions associated with noise and industrial data are performed to prove the usefulness and effectiveness of this study.
| Original language | English |
|---|---|
| Pages (from-to) | 19-31 |
| Number of pages | 13 |
| Journal | Advances in Engineering Software |
| Volume | 100 |
| DOIs | |
| Publication status | Published - 2016 Oct 1 |
| Externally published | Yes |
Keywords
- Matlab toolbox
- Penalized spline regression
- Random sampling
- Sensitivity analysis
- Uncertainty quantification
ASJC Scopus subject areas
- Software
- Engineering(all)
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Cite this
Vu-Bac, N., Lahmer, T., Zhuang, X., Nguyen-Thoi, T., & Rabczuk, T. (2016). A software framework for probabilistic sensitivity analysis for computationally expensive models. Advances in Engineering Software, 100, 19-31. https://doi.org/10.1016/j.advengsoft.2016.06.005