reliability
Last update: 05.08.2026class reliability.LineSearchMethod
Enumeration
Bisection = reliability.LineSearchMethod.Bisection
RegulaFalsi = reliability.LineSearchMethod.RegulaFalsi
class reliability.ReliabilityAdaptiveSampling
Adaptive sampling implements importance sampling using multiple adaptation steps. The first step scales all designs by an initial blow up factor in standard-normal space. The objective of the scaling is to obtain a reasonable number of samples in the failure domain during the 1st step. Hence, the blow up factor should be chosen according to the failure probability pf the problem. While generating the samples one ignores samples which are further away than
max_allowed_sigma_
from the origin in standard normal space. The reason is that importance weights become inaccurate for samples being that far away. After sampling the user-defined number of samples in a single step, the median and covariance matrix of the sampling density function of the next step will be determined according to the statistics of the samples lying in the failure domain. A modified eigenvalue decomposition of the covariance matrix ensures positive definiteness. Furthermore, if the C.O.V. of Pf is relatively large, the covariance matrix is scaled such that it is closer to the identity matrix. The user defines the number of steps and the number of samples per step. If any of the solver calls fails, the corresponding sample is either ignored or (if one tells the algorithm to interpret the failed run as a failure event) is accounted to the failure domain.
__init__()
get_current_step() → int
Returns the current iteration step number.
get_current_weights() → vector_type
Returns the current importance sampling weights.
class reliability.ReliabilityBase
__init__()
Raises an exception This class cannot be instantiated from Python
adapt()
Creates internally the next set of designs. This is called within the loop after Appraise and prepares the next call of get_next_designs.
appraise()
Performs a search step by the internal reliability algorithm. This is called after simulation and setting results and used inputs. The method reads all given information (and eventually performs one internal search step). After calling this method it should be possible to check the state of the iteration by is_terminated().
can_provide_probability_of_failure() → bool
Returns true when the probability of failure can be calculated.
finalize()
Finalizes the iteration. Does whatever is neccessary to finalize - delete pointers, collect data…
get_criteria_success_info() → bitset_type
get_estimators_std_dev() → float
Returns the standard deviation of the estimator of the probability of failure. It should be called after finalize(). If this quantity does not make sense (i.e. in case of FORM), it returns zero.
get_inputs() → matrix_type
get_iteration_name() → str
Returns the name of the current iteration.
get_next_design_ids() → uintVec
Returns the ids for the next set of design vectors for which responses must be computed. This method is called at the begin of the iteration loop. It returns a reference to the internally stored set of infos of awaiting designs.
get_next_design_infos() → uintVec
Returns some information for the next set of design vectors for which responses must be computed. This method is called at the begin of the iteration loop. It returns a reference to the internally stored set of infos of awaiting designs.
get_next_designs() → matrix_type
Returns the next set of design vectors for which responses must be computed. This method is called at the begin of the iteration loop. It returns a reference to the internally stored set of awaiting designs.
get_num_awaiting_designs() → int
get_num_computed_samples() → int
get_num_function_calls() → int
get_num_inputs() → int
get_num_safety_margins() → int
get_num_successfully_computed_samples() → int
get_probability_of_failure() → float
Returns the probability of failure.
get_reliability_index() → float
The default implementation calls GetProbabilityOfFailure and computes beta from that. It should be called after finalize().
get_rv_set() → RVSet
get_safety_margins() → matrix_type
get_success_info_as_failure_event() → bitset_type
has_several_iterations() → bool
Returns True if the algorithm produces several design sets.
has_total_design_set() → bool
Returns True if the algorithm produces a total design set.
initialize(arg2: SettingsBase)
(re)sets the algorithm using the given definition data (“settings”). It must be called at least once before starting the iteration loop. It may be called several times during the lifetime of an algorithm object (then clears old data, reallocates and resets).
is_converged() → bool
Returns true if the iteration loop has converged. This method returns true if the algorithm has terminated and converged.
is_terminated() → bool
Returns true if the iteration loop must be terminated. This method returns true if the algorithm has finished. If it either converged (or diverged or any other termination criterion is satisfied).
restart()
(re)sets the algorithm without touching the settings and maybe time-consuming pre-calculations. Initialize must be called at least once before.
set_criteria_success_info(arg2: bitset_type)
Sets the success flags of the actually computed designs. It must be called before Appraise. The input argument tells the algorithm for which design point vector the external solver failed (or returned senseless results).
set_inputs(arg2: matrix_type)
Sets the design point vectors of the actually computed designs. It must be called before Appraise.
set_safety_margins(arg2: matrix_type)
Sets the safety margin function(s) of the actually computed designs. It must be called before Appraise.
set_success_info_as_failure_event(arg2: bitset_type)
Sets the failure event flags of the actually computed designs without successful solver run. It must be called before Appraise. The input argument tells the algorithm for which design point vector the value success_info_[i]=false can be interpreted as a failure event.
class reliability.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 reliability.SettingsARSMDS
Reliability algorithm settings for ARSM with Directional Sampling.
__init__()
__init__(arg2: RVSet, arg3: int)
property blow_up
property num_directions
property num_steps
property num_supports_initial_step
property num_supports_per_step
class reliability.SettingsAdaptiveSampling
Reliability algorithm settings for Adaptive Sampling.
__init__()
__init__(arg2: RVSet, arg3: int)
property accuracy
property automatic_sample_size
property initial_scale_factor
property num_designs_per_sample
property num_steps
property num_total_samples_per_step
property use_standard_density
property write_weights
class reliability.SettingsBase
Base class that define the reliability problem.
__init__()
Raises an exception This class cannot be instantiated from Python
check_settings()
Check if settings are correct. Raises an error if they are not.
property num_estimated_function_calls
property num_inputs
property num_safety_margins
property rand_generator_seed
property rvset
set_defaults()
property write_histories
property write_input_importance
class reliability.SettingsDirectionalSampling
Reliability algorithm settings for directional sampling.
__init__()
__init__(arg2: RVSet, arg3: int)
property linesearch_accuracy
property linesearch_method
property linesearch_num_parallel_presample
property num_directions
property num_parallel
property search_direction_sampling_method
class reliability.SettingsFORM
Reliability algorithm settings for Form/OptimizerNLPQLP
__init__()
__init__(arg2: RVSet, arg3: int)
property max_search_runs
property num_presamples
property optimizer_settings
FORM’s NLPQLP settings. Cannot be copied.
class reliability.SettingsISPUD
Reliability algorithm settings for ISPUD( Importance sampling using the design point).
__init__()
__init__(arg2: RVSet, arg3: int)
property accuracy
property automatic_sample_size
property form_settings
ISPUD’s FORM settings. Cannot be copied.
property num_designs_per_sample
property num_total_samples
property use_median_sampling
property use_start_designs
property vary_discrete_rvs
property write_weights
class reliability.SettingsMonteCarlo
Reliability algorithm settings for Monte Carlo.
