UQ[py]Lab Examples

Basic modelling - Multiple outputs

Basic modelling - Model parameters

Basic modelling - Model definition

Reliability analysis - Active learning: Asynchronous learning

Reliability analysis - Active learning: Advanced usage

Reliability analysis - Active learning: Basic usage

Polynomial chaos expansions - Bootstrap PCE

Polynomial chaos expansions - PCE with arbitrary distributions

Polynomial chaos expansions - PCE from existing data

Polynomial chaos expansions - multiple outputs

Polynomial chaos expansions - experimental design options

Reliability analysis - Time-variant reliability

Reliability analysis - System reliability analysis

Reliability analysis - Simple R-S limit state function

Sensitivity analysis - Dependent inputs

Sensitivity analysis - Multiple outputs

Sensitivity analysis - High-dimensional Sobol'

Sensitivity analysis - Methods overview

Kriging - Gaussian process regression

Kriging - Existing data 2: Boston housing

Kriging - Existing data 1: Truss

Kriging - Multiple input dimensions

Kriging - Trend types

Kriging - Estimation methods and optimisation strategies

Probabilistic modeling - marginals and copula inference

Probabilistic modeling - marginals inference

Probabilistic modeling - block independence

Probabilistic modeling - marginals and Vine copula

Probabilistic modeling - marginals and Pair copula

Polynomial chaos expansions - estimation of statistical moments

Probabilistic modeling - sampling strategies

Kriging - Multiple outputs

Reliability analysis - 2D hat function

Sensitivity analysis - Sobol' indices

Kriging - 1D example

Probabilistic modeling - marginals and Gaussian copula

Polynomial chaos expansions - calculation strategies