AK_MCSAlgorithm

class otak.AK_MCSAlgorithm(event, n_MC, n_DoE, sim_budget, basis, cov_model, u_criterion=2, verbose=False)

Class implementing AK MCS algorithm

Event

ThresholdEvent based on composite vector of input variables on limit state function

N_MC

integer, number of MCS samples

N_DoE

integer, number of samples in initial Kriging DoE

Sim_budget

integer, total simulation budget available

Basis

basis of kriging model

Cov_model

covariance model of kriging

U_criterion

float, threshold value for u criterion

Verbose

verbosity parameter

Methods

compute_U(my_krig, list_id_evaluated)	Function computing the infill criterion
compute_proba()	Function computing failure probability using AK-IS
getCoefficientOfVariation()	Accessor to Coefficient of Variation
getDoE()	Accessor to the Design of Experiments, openturns.Sample
getFailureProbability()	Accessor to the computed failure probability
getKrigingModel()	Accessor to the Kriging model, openturns.KrigingResult
getMonteCarloSamples()	Accessor to Monte-Carlo samples, openturns.Samples
getSimBudget()	Accessor to the simulation budget

__init__(event, n_MC, n_DoE, sim_budget, basis, cov_model, u_criterion=2, verbose=False)

compute_U(my_krig, list_id_evaluated)

Function computing the infill criterion

My_krig

Kriging models, openturns.KrigingResult

List_id_evaluated

list of evaluated openturns.Sample

compute_proba()

Function computing failure probability using AK-IS

getCoefficientOfVariation()

Accessor to Coefficient of Variation

getDoE()

Accessor to the Design of Experiments, openturns.Sample

getFailureProbability()

Accessor to the computed failure probability

getKrigingModel()

Accessor to the Kriging model, openturns.KrigingResult

getMonteCarloSamples()

Accessor to Monte-Carlo samples, openturns.Samples

getSimBudget()

Accessor to the simulation budget