UQLab: The Framework for Uncertainty Quantification. UQLab is a Matlab-based software framework designed to bring state-of-the art uncertainty quantification (UQ) techniques and algorithms to a large audience. UQLab is not simply an umpteenth toolbox for UQ, but a framework: not only it offers you an extensive arsenal of built-in types of analyses and algorithms but it also provides a powerful new way of developing and implementing your own ideas. The project originated in 2013, when Prof. Bruno Sudret founded the Chair of Risk, Safety and Uncertainty Quantification at ETH Zurich, and decided to gather the results of a decade of his research into a single software tool. UQLab provides now the software backbone of the Chair’s research, allowing for seamless integration between the many research fields engaged by its members, e.g. metamodeling (polynomial chaos expansions, Gaussian process modelling, a.k.a. Kriging, low-rank tensor approximations), rare event estimation (structural reliability), global sensitivity analysis, Bayesian techniques for inverse problems, etc. After more than two years of development it was decided to open the platform to other research institutions, in an effort to increase the awareness of the scientific community regarding the fundamental aspects of uncertainty quantification. The first closed beta version is online since July 1st, 2015.
Keywords for this software
References in zbMATH (referenced in 43 articles )
Showing results 41 to 43 of 43.
- Schöbi, Roland; Sudret, Bruno: Uncertainty propagation of p-boxes using sparse polynomial chaos expansions (2017)
- Konakli, Katerina; Sudret, Bruno: Polynomial meta-models with canonical low-rank approximations: numerical insights and comparison to sparse polynomial chaos expansions (2016)
- Nagel, Joseph B.; Sudret, Bruno: Spectral likelihood expansions for Bayesian inference (2016)