UQ[Py]Lab v. 1.0 is finally out

End of September the first official stable release of UQ[py]Lab V1.0, a free, comprehensive python tool designed to support researchers, engineers, and data scientists in uncertainty quantification, based on the external page UQLab framework. UQLab is the general-purpose uncertainty quantification software developed at ETH Zurich (Switzerland) since 2013, with more than 7,000 users worldwide.

On top of a number of back-end and performance improvements and bug fixes, this milestone version introduces a new metamodeling tool, Polynomial Chaos-Kriging, and Bayesian model calibration and inversion. This complements the tools already available in V0.9 for surrogate modelling (polynomial chaos expansions, Gaussian processes / Kriging), sensitivity analysis, reliability analysis including active learning methods.

Thanks to Adéla Hlobilová for the huge work behind this release, in particular the comprehensive documentation, the numerous new examples, the fancy graphics and ... comprehensive testing!

Thanks to Stefano Marelli for the development of the overarching cloud architecture (with Christos Lataniotis for the beta version) and the overall coordination of this project!