Gaussian process modeling is a flexible and robust technique to build fast surrogate models based on small experimental designs
When the performance of a system is affected by uncertainties on its characteristics and/or its environment, reliability can be assessed by computing probabilities of failure.
UQLab offers state-of-the-art reliability algorithms and a powerful modular framework
for active learning reliability.
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.
Sensitivity analysis can identify the driving factors that most influence the response of a model
Polynomial chaos expansions (PCE), one of the most powerful and versatile surrogate models available today