New examples have been added in our gallery!
Make sure to check them out here.
In this version of UQ[py]Lab several updates and fixes have been introduced. Most notable entries:
Polynomial chaos expansions (PCE), one of the most powerful and versatile surrogate models available today
Sensitivity analysis can identify the driving factors that most influence the response of a model
Probabilistic modelling lies at the core of uncertainty quantification. Full support for complex probabilistic models based on copula and marginal representation. Powerful data-driven inference module.
Chair of Risk, Safety and Uncertainty Quantification
Stefano Franscini-Platz 5
CH-8093 Zurich
Switzerland