/usr/include/openturns/swig/SORM

%feature("docstring") OT::SORM
"Second Order Reliability Method (SORM).

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

Parameters
----------
nearestPointAlgorithm : :class:`~openturns.OptimizationAlgorithm`
    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
-----
See :class:`~openturns.Analytical` for the description of the first steps of
the SORM analysis.

The Second Order Reliability Method (SORM) consists in approximating the limit
state surface in U-space at the design point :math:`P^*` by a quadratic
surface. SORM is usually more accurate than FORM e.g. in case when the event
boundary is highly curved.

Let us denote by :math:`n` the dimension of the random vector :math:`\\\\vect{X}`
and :math:`(\\\\kappa_i)_{1 \\\\leq i \\\\leq n-1}` the :math:`n-1` main curvatures of
the limit state function at the design point in the standard space.

Several approximations of the failure probability :math:`P_f` are available in
the standard version of OpenTURNS, detailed here in the case where the origin
of the standard space does not belong to the failure domain :

- Breitung's formula :

  .. _Breitung_formula:

  .. math ::

      P_{Breitung} = E(-\\\\beta_{HL})\\\\prod_{i=1}^{n-1} \\\\frac{1}{\\\\sqrt{1 + \\\\beta_{HL}\\\\kappa_i}}

  :math:`E` the marginal cumulative density function of the spherical
  distributions in the standard space and :math:`\\\\beta_{HL}` is the Hasofer-Lind
  reliability index, defined as the distance of the design point
  :math:`\\\\vect{u}^*` to the origin of the standard space.

- Hohen Bichler's formula is an approximation of the previous equation :

  .. _Hohenbichler_formula:

  .. math ::

      \\\\displaystyle P_{Hohenbichler} = \\\\Phi(-\\\\beta_{HL})
       \\\\prod_{i=1}^{n-1} \\\\left(
                         1 + \\\\frac{\\\\phi(\\\\beta_{HL})}{\\\\Phi(-\\\\beta_{HL})}\\\\kappa_i
                         \\\\right) ^{-1/2}

  where :math:`\\\\Phi` is the cumulative distribution function of the
  standard 1D normal distribution and :math:`\\\\phi` is the standard Gaussian
  probability density function.

- Tvedt's formula :

  .. _Tvedt_formula:

  .. math ::

      \\\\left\\\\{
        \\\\begin{array}{lcl}
          \\\\displaystyle P_{Tvedt} & = & A_1 + A_2 + A_3 \\\\\\\\
          \\\\displaystyle A_1 & = & \\\\displaystyle
            \\\\Phi(-\\\\beta_{HL}) \\\\prod_{i=1}^{N-1} \\\\left( 1 + \\\\beta_{HL} \\\\kappa_i \\\\right) ^{-1/2}\\\\\\\\
          \\\\displaystyle A_2 & = & \\\\displaystyle
            \\\\left[ \\\\beta_{HL} \\\\Phi(-\\\\beta_{HL}) - \\\\phi(\\\\beta_{HL}) \\\\right]
            \\\\left[ \\\\prod_{j=1}^{N-1} \\\\left( 1 + \\\\beta_{HL} \\\\kappa_i \\\\right) ^{-1/2} -
                   \\\\prod_{j=1}^{N-1} \\\\left( 1 + (1 + \\\\beta_{HL}) \\\\kappa_i \\\\right) ^{-1/2}
            \\\\right ] \\\\\\\\
          \\\\displaystyle A_3 & = & \\\\displaystyle (1 + \\\\beta_{HL})
            \\\\left[ \\\\beta_{HL} \\\\Phi(-\\\\beta_{HL}) - \\\\phi(\\\\beta_{HL}) \\\\right]
            \\\\left[ \\\\prod_{j=1}^{N-1} \\\\left( 1 + \\\\beta_{HL} \\\\kappa_i \\\\right) ^{-1/2} -
                   {\\\\cR}e \\\\left( \\\\prod_{j=1}^{N-1} \\\\left( 1 + (i + \\\\beta_{HL}) \\\\kappa_j \\\\right) ^{-1/2}
            \\\\right)\\\\right ]
        \\\\end{array}
      \\\\right.

  where :math:`{\\\\cR}e(z)` is the real part of the complex number :math:`z` and
  :math:`i` the complex number such that :math:`i^2 = -1`.

The evaluation of the failure probability is stored in the data structure
:class:`~openturns.SORMResult` recoverable with the :meth:`getResult` method.

See also
--------
Analytical, AnalyticalResult, FORM, StrongMaximumTest, SORMResult

Examples
--------
>>> import openturns as ot
>>> myFunction = ot.SymbolicFunction(['E', 'F', 'L', 'I'], ['-F*L^3/(3*E*I)'])
>>> myDistribution = ot.Normal([50.0, 1.0, 10.0, 5.0], [1.0]*4, ot.IdentityMatrix(4))
>>> vect = ot.RandomVector(myDistribution)
>>> output = ot.RandomVector(myFunction, vect)
>>> event = ot.Event(output, ot.Less(), -3.0)
>>> # We create an OptimizationAlgorithm algorithm
>>> solver = ot.AbdoRackwitz()
>>> algo = ot.SORM(solver, event, [50.0, 1.0, 10.0, 5.0])
>>> algo.run()
>>> result = algo.getResult()"

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

%feature("docstring") OT::SORM::getResult
"Accessor to the result of SORM.

Returns
-------
result : :class:`~openturns.SORMResult`
    Structure containing all the results of the SORM analysis."

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

%feature("docstring") OT::SORM::setResult
"Accessor to the result of SORM.

Parameters
----------
result : :class:`~openturns.SORMResult`
    Structure containing all the results of the SORM analysis."

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

%feature("docstring") OT::SORM::run
"Evaluate the failure probability.

Notes
-----
Evaluate the failure probability and create a :class:`~openturns.SORMResult`,
the structure result which is accessible with the method :meth:`getResult`."
libopenturns-dev 1.9-5 / usr / include / openturns / swig / SORM_doc.i
