Developers - S. Kucherenko, O. Zaccheus
SobolGSA is general purpose GUI driven global sensitivity analysis and metamodeling software. It can be used to compute various sensitivity measures and/or to develop metamodels.
There is a choice of three different metamodeling techniques, including Quasi Random Sampling-High dimensional model representation (QRS-HDMR) method both with regression and projection methods and radial basis function method. The improved QRS-HDMR method is described in [1-3]. SobolGSA can be used to construct metamodels either from explicitly known models or directly from data produced by "black-box" models. SobolGSA can be applied to both static and time-dependent problems. It can handle several outputs for analysis; each output can be time-dependent. Developed metamodels are produced in a form of self-contained MATLAB or C# files which can be used as accurate, reliable and very fast surrogates of the original CPU-expensive full scale models.
SobolGSA is a tool for global sensitivity analysis (GSA). GSA methods evaluate the effect of a factor while all other factors are varied as well and thus they account for interactions between variables and do not depend on the choice of a nominal point like local sensitivity analysis methods. The set of available global sensitivity analysis techniques includes screening methods (Morris measure), variance (Sobol’ indices, FAST) and derivative based sensitivity measures [4-8].
Sensitivity measures can be computed directly using MC/QMC techniques or by building metamodels first and then computing sensitivity measures using metamodels. The second approach is a much cheaper in terms of required function evaluations.
All techniques implemented in SobolGSA make use of Quasi Monte Carlo sampling based on Sobol sequences [9,10]. The software has a user friendly interface for inputs and presenting results. SobolGSA can be linked to MATLAB and other packages. Software includes detailed manuals, case studies and a set of test problems with descriptions for benchmarking and training.
Comments and questions can be sent to email@example.com.
- Kucherenko S. SobolHDMR: a general-purpose modeling software Methods Mol Biol. (2013) 1073:191-224. doi: 10.1007/978-1-62703-625-2_16
- Feil B., Kucherenko S., Shah N. Comparison of Monte Carlo and Quasi Monte Carlo Sampling Methods in High Dimensional Model Representation, SIMUL 2009, Porto, Portugal.
- Zuniga M., Kucherenko S., Shah N. Metamodelling with independent and dependent inputs, Computer Physics Communications, 184, 6 (2013) 1570-1580.
- Sobol’ I., Kucherenko S. Global Sensitivity Indices for Nonlinear Mathematical Models. Review, Wilmott, 1 (2005) 56-61.
- Kucherenko S., Feil B., Shah N., Mauntz W. The identification of model effective dimensions using global sensitivity analysis Reliability Engineering and System Safety 96 (2011) 440–449.
- Kucherenko S., Tarantola S., Annoni P. Estimation of global sensitivity indices for models with dependent variables, Computer Physics Communications, 183 (2012) 937–946.
- Kucherenko S., Rodriguez-Fernandez M., Pantelides C., Shah N. Monte Carlo evaluation of derivative based global sensitivity measures. Reliability Engineering and System Safety 94, 7 (2009) 1135-1148.
- Sobol’ I.M., Kucherenko S. Derivative based Global Sensitivity Measures and their link with global sensitivity indices, Mathematics and Computers in Simulation, 79, 10 (2009) 3009-3017.
- Sobol’ I.M., Asotsky D., Kreinin A., Kucherenko S. Construction and Comparison of High-Dimensional Sobol’ Generators, 2011, Wilmott Journal (2012) 64-79.
- Kucherenko S., Albrecht D., Saltelli A. Exploring multi-dimensional spaces: a Comparison of Latin Hypercube and Quasi Monte Carlo Sampling Techniques, to be published in Reliability Engineering and System Safety, 2016 arXiv:1505.02350.
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