"Make uncertainty quantification available for anybody,

in any field of applied science and engineering"

  • MATLAB®-based Uncertainty Quantification framework

  • State-of-the art, highly optimized open source algorithms

  • Fast learning curve for beginners

  • Modular structure, easy to extend

  • Exhaustive documentation

  • 14 January 2020: Maxime Fays, a master’s student in Modelling, Scientific Computing and Image Analysis at the Université Grenoble Alpes (France), is the 2500th user of UQLab since the first public release in July 2015. Maxime currently uses UQLab as a support to a course related to rare event simulation. As a future PhD student in computational biomechanics applied to biomedical stents, he considers using UQLab’s comprehensive surrogate modelling modules.

  • 19 September 2019: UQLab Modules Rel 1.3 is released. The release includes a new reliability-based design optimization (RBDO) module, a statistical inference module, and an overhauled Input module, now with much more advanced dependence modelling. Check out the Release Notes  for more details.

News archive 


UQLab is a general purpose Uncertainty Quantification framework developed at ETH Zurich (Switzerland). It is made of open-source scientific modules which are smoothly connected through UQLab to carry out uncertainty propagation through Monte Carlo sampling, sensitivity analysis, reliability analysis (computation of rare event probabilities), build surrogate models (polynomial chaos expansions, Kriging, low-rank tensor approximations, etc.) and more.

UQLab, the content management system that allows for an easy use of UQLab modules, can be downloaded here .

It is completely free for academic users: academic registration form

For non-academic users: commercial registration form .

UQLab is developed at the Chair of Risk, Safety and Uncertainty Quantification of ETH Zurich under the supervision of Prof. B. Sudret and Dr. S. Marelli.

MATLAB® is a registered trademark of The MathWorks, Inc. 

Reliability-based Design Opt.

The reliability-based design optimization (RBDO) module offers a set of state-of-the-art algorithms to solve various types of optimization problems under probabilistic constraints

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