libopenturns-dev 1.7-3 / usr / include / openturns / swig / Analytical_doc.i

%feature("docstring") OT::Analytical
"Base class to evaluate the probability of failure of a system.

Available constructors:
    Analytical(*nearestPointAlgorithm, event, physicalStartingPoint*)

Parameters
----------
nearestPointAlgorithm : :class:`~openturns.OptimizationSolver`
    Optimization algorithm used to research the design point.
event : :class:`~openturns.Event`
    Failure event.
physicalStartingPoint : sequence of float
    Starting point of the optimization algorithm, declared in the physical
    space.

Notes
-----
Used in reliability analysis, Analytical is a base class for the approximation
methods :class:`~openturns.FORM` and :class:`~openturns.SORM` enabling to
evaluate the failure probability of a system.
A failure event is defined as follows :
:math:`\\\\cD_f = \\\\{\\\\vect{X} \\\\in \\\\Rset^n \\\\, | \\\\, g(\\\\vect{X},\\\\vect{d}) \\\\le 0\\\\}`
where :math:`\\\\vect{X}` denotes a random input vector representing the sources
of uncertainties, :math:`\\\\vect{d}` is a determinist vector representing the
fixed variables. :math:`g(\\\\vect{X},\\\\vect{d})` is the limit state function of
the model separating the failure domain from the safe domain.
Considering :math:`f_\\\\vect{X}(\\\\vect{x})` the joint probability density function
of the random variables :math:`\\\\vect{X}`, the probability of failure of the
event :math:`\\\\cD_f` is :

.. math::

    P_f = \\\\int_{g(\\\\vect{X},\\\\vect{d})\\\\le 0}f_\\\\vect{X}(\\\\vect{x})d\\\\vect{x}

The analytical methods use an isoprobabilistic transformation to move from
the physical space to the standard normal space (U-space) where distributions
are spherical (invariant by rotation by definition), with zero mean, unit
variance and unit correlation matrix. The usual isoprobabilistic
transformations are the Generalized Nataf transformation and the Rosenblatt one.

In that new U-space, the event has the new expression defined
from the transformed limit state function of the model
:math:`G : \\\\cD_f = \\\\{\\\\vect{U} \\\\in \\\\Rset^n \\\\, | \\\\, G(\\\\vect{U}\\\\,,\\\\,\\\\vect{d}) \\\\le 0\\\\}`
and its boundary :
:math:`\\\\{\\\\vect{U} \\\\in \\\\Rset^n \\\\, | \\\\,G(\\\\vect{U}\\\\,,\\\\,\\\\vect{d}) = 0\\\\}`.
Then, the event probability :math:`P_f` rewrites :

.. math::

    P_f = \\\\Prob{G(\\\\vect{U}\\\\,,\\\\,\\\\vect{d})\\\\leq 0}
        = \\\\int_{\\\\Rset^n} \\\\boldsymbol{1}_{G(\\\\vect{u}\\\\,,\\\\,\\\\vect{d}) \\\\leq 0}\\\\,f_{\\\\vect{U}}(\\\\vect{u})\\\\,d\\\\vect{u}

where :math:`f_{\\\\vect{U}}` is the density function of the distribution in the
standard space.

The analytical methods rely on the assumption that most of the contribution
to :math:`P_f` comes from points located in the vicinity of a particular point
:math:`P^*`, the **design point**, defined in the U-space as the point located
on the limit state surface verifying the event of maximum likelihood. Given the
probabilistic characteristics of the U-space, :math:`P^*` has a geometrical
interpretation: it is the point located on the event boundary and at minimal
distance from the origin of the U-space. Thus, considering :math:`\\\\vect{u}^*`
its coordinates in the U-space, the design point is the result of the
constrained optimization problem :

.. math::

    \\\\vect{u}^* = argmin \\\\{||\\\\vect{u}|| \\\\, | \\\\, G(\\\\vect{u}) = 0 \\\\}

Then the limit state surface is approximated in the standard space by a linear
surface (:class:`~openturns.FORM`) or by a quadratic surface
(:class:`~openturns.SORM`) at the design point in order to evaluate the
failure probability. For more information on this evaluation, see the
documentation associated with these two methods.

The result of the optimization problem is recoverable thanks to the method
:meth:`getAnalyticalResult`.

The unicity and the strongness of the design point can be checked thanks to the
:class:`Strong Maximum Test <openturns.StrongMaximumTest>`.

See also
--------
FORM, SORM, StrongMaximumTest, Event, StandardEvent, AnalyticalResult

Examples
--------
>>> import openturns as ot
>>> myFunction = ot.NumericalMathFunction(['E', 'F', 'L', 'I'], ['d'], ['-F*L^3/(3*E*I)'])
>>> myDistribution = ot.Normal([50., 1., 10., 5.], [1.]*4, ot.IdentityMatrix(4))
>>> vect = ot.RandomVector(myDistribution)
>>> output = ot.RandomVector(myFunction, vect)
>>> myEvent = ot.Event(output, ot.Less(), -3.0)
>>> # We create an OptimizationSolver algorithm
>>> myCobyla = ot.Cobyla()
>>> myAlgo = ot.Analytical(myCobyla, myEvent, [50., 1., 10., 5.])"

// ---------------------------------------------------------------------

%feature("docstring") OT::Analytical::getAnalyticalResult
"Accessor to the result.

Returns
-------
result : :class:`~openturns.AnalyticalResult`
    Result structure which contains the results of the optimisation problem."

// ---------------------------------------------------------------------

%feature("docstring") OT::Analytical::getNearestPointAlgorithm
"Accessor to the optimization algorithm used to find the design point.

Returns
-------
algorithm : :class:`~openturns.OptimizationSolver`
     Optimization algorithm used to research the design point."

// ---------------------------------------------------------------------

%feature("docstring") OT::Analytical::setNearestPointAlgorithm
"Accessor to the optimization algorithm used to find the design point.

Parameters
----------
algorithm : :class:`~openturns.OptimizationSolver`
     Optimization algorithm used to research the design point."

// ---------------------------------------------------------------------

%feature("docstring") OT::Analytical::getPhysicalStartingPoint
"Accessor to the starting point of the optimization algorithm.

Returns
-------
point : :class:`~openturns.NumericalPoint`
    Starting point of the optimization algorithm, declared in the physical
    space."

// ---------------------------------------------------------------------

%feature("docstring") OT::Analytical::setPhysicalStartingPoint
"Accessor to the starting point of the optimization algorithm.

Parameters
----------
point : sequence of float
    Starting point of the optimization algorithm, declared in the physical
    space."

// ---------------------------------------------------------------------

%feature("docstring") OT::Analytical::getEvent
"Accessor to the event of which the probability is calculated.

Returns
-------
event : :class:`~openturns.Event`
    Event of which the probability is calculated."

// ---------------------------------------------------------------------

%feature("docstring") OT::Analytical::setEvent
"Accessor to the event of which the probability is calculated.

Parameters
----------
event : :class:`~openturns.Event`
    Event of which the probability is calculated."

// ---------------------------------------------------------------------

%feature("docstring") OT::Analytical::run
"Perform the research of the design point.

Notes
-----
Performs the research of the design point and creates a
:class:`~openturns.AnalyticalResult`, the structure result which is
accessible with the method :meth:`getAnalyticalResult`."