pystra.reliability.monte_carlo.MonteCarlo#

Use it: User guide · Worked example · Theory

class MonteCarlo(model, limit_state, *, options=None, rng=None)[source]#

Bases: AnalysisObject

Base implementation for Monte Carlo simulation analyses.

Random samples in physical or standard space estimate the failure probability from limit-state evaluations [Nowak2000] [Faber2009] [Lemaire2010]. Use CrudeMonteCarlo for direct sampling or a specialized subclass for a different sampling scheme.

Parameters:

  • model (StochasticModel) – Named physical variables and their joint probability model.

  • limit_state (LimitState) – Physical response evaluated at the sample points.

  • options (SimulationOptions | None) – Sampling, convergence and transformation settings.

  • rng (int | integer | Sequence[int] | ndarray | SeedSequence | BitGenerator | Generator | None) – Random source; NumPy’s global generator is not used. A seed recreates the same stream on every run, a generator advances its own state, and None draws fresh entropy.

Methods

init_run

Initialize the model's isoprobabilistic transformation.
init_run()#

Initialize the model’s isoprobabilistic transformation.

Uses the explicit copula or calibrates the legacy Gaussian Nataf correlation. Must be called at the start of every run() method in subclasses.

Return type:

None