UncertaintyQuantification.jl

Appearance

0.0k

Reliability ​

Index ​

Types ​

UncertaintyQuantification.FORM Type
julia
FORM(n::Integer=10,tol::Real=1e-3,fdm::FiniteDifferencesMethod=CentralFiniteDifferences(3))

used to perform the first order reliability method using the HLRF algorithm with n iterations and tolerance tol. Gradients are estimated through fdm.

References

[12]

source
UncertaintyQuantification.DoubleLoop Type
julia
DoubleLoop(lb::AbstractSimulation, ub::AbstractSimulation)
source
UncertaintyQuantification.RandomSlicing Type
julia
RandomSlicing(lb::AbstractSimulation, ub::AbstractSimulation)
source

Methods ​

UncertaintyQuantification.probability_of_failure Method
julia
probability_of_failure(models::Union{Vector{<:UQModel},UQModel},performance::Function),inputs::Union{Vector{<:UQInput},UQInput},sim::FORM)

Perform a reliability analysis using the first order reliability method (FORM), see FORM. Returns the estimated probability of failure pf, the reliability index β and the design point dp.

Examples

pf, β, dp = probability_of_failure(model, performance, inputs, sim)
source
UncertaintyQuantification.probability_of_failure Method
julia
probability_of_failure(models::Union{Vector{<:UQModel},UQModel},performance::Function),inputs::Union{Vector{<:UQInput},UQInput},sim::AbstractMonteCarlo)

Perform a reliability analysis with a standard Monte Carlo simulation. Returns the estimated probability of failure pf, the standard deviation σ and the DataFrame containing the evaluated samples. The simulation sim can be any instance of AbstractMonteCarlo.

Examples

pf, σ, samples = probability_of_failure(model, performance, inputs, sim)
source