Reliability & Sensitivity

Central dispersion

Evaluate the mean of a random vector by simulations

Analyse the central tendency of a cantilever beam

Estimate moments from Taylor expansions

Reliability

Estimate a probability with Latin Hypercube Sampling

Simulate an Event

Estimate a probability with Monte Carlo

Use a randomized QMC algorithm

Use the Adaptive Directional Sampling Algorithm

Use the post-analytical importance sampling algorithm

Use the Directional Sampling Algorithm

Estimate a flooding probability

Specify a simulation algorithm

Create a threshold event

Use the Importance Sampling algorithm

Estimate a probability with Monte-Carlo on axial stressed beam: a quick start guide to reliability

Exploitation of simulation algorithm results

Use the FORM algorithm in case of several design points

Subset Sampling

Use the FORM - SORM algorithms

Create a domain event

Test the design point with the Strong Maximum Test

Time variant system reliability problem

Create unions or intersections of events

Axial stressed beam : comparing different methods to estimate a probability

An illustrated example of a FORM probability estimate

Reliability processes

Create an event based on a process

Estimate a process-based event probability

Sensitivity analysis

FAST sensitivity indices

Cobweb graph as sensitivity tool

Estimate Sobol’ indices for a function with multivariate output

Sobol’ sensitivity indices from chaos

Use the ANCOVA indices

Estimate Sobol’ indices for the Ishigami function by a sampling method: a quick start guide to sensitivity analysis

Design of experiments

Create a composite design of experiments

Create a Monte Carlo design of experiments

Probabilistic design of experiments

Create a Gauss product design

Create a random design of experiments

Create mixed deterministic and probabilistic designs of experiments

Create a design of experiments with discrete and continuous variables

The PlotDesign method

Deterministic design of experiments

Create a deterministic design of experiments

Generate low discrepancy sequences

Various design of experiments in OpenTURNS

Optimize an LHS design of experiments