py_reliability_montecarlo

class py_reliability_montecarlo.ReliabilityMonteCarlo

This algorithm implements Monte Carlo sampling. The algorithm can be controlled either by a predefined number of samples, or by a desired accuracy (c.o.v.) of the estimator of Pf (or of (1-Pf) if Pf>0.5). In the latter case, the number of required samples will be selected by the algorithm during the iteration. The convergence test is carried out after each request of n parallel designs. If the desired accuracy can not be reached within the maximum allowed number of samples, the algorithm terminates without success. If any of the solver requests fails (success_info=false for a specific design) the algorithm either ignores this design, or it counts it as a failure event.

__init__()

class py_reliability_montecarlo.SettingsMonteCarlo

Reliability algorithm settings for Monte Carlo.

__init__()

__init__(arg2: RVSet, arg3: int)

property accuracy

property automatic_sample_size

property min_num_samples

property num_designs_per_sample

property num_total_samples

property scaling_factor