Bayesian inference is a powerful tool for probabilistic model calibration and inverse problems. UQ[py]Lab offers a flexible and intuitive way to set-up and solve Bayesian inverse problems.
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. They include:
On top of these well-known algorithms, the modular design of the RBDO module allows the user to set up customized solution schemes by combining all of the reliability, surrogate modeling, and optimization techniques available in UQLab.
Support vector machines (SVM) come from machine learning and allow one to build predictive models from data. In the context of uncertainty quantification, SVM for regression (SVR) can be used as surrogate models of complex simulators using designs of computer experiments. SVM for classification (SVC) can be used in the context of reliability analysis.
Polynomial Chaos-Kriging (PC-Kriging) associates the global approximation behavior of polynomial chaos expansions and the local accuracy of Kriging to provide a highly accurate surrogate model at low computational costs.
Canonical low-rank approximations (LRA) are a powerful alternative to polynomial chaos expansions that are particularly effective in high dimension.
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