QuadraticTaylor

class QuadraticTaylor(*args)

Second order polynomial response surface by Taylor expansion.

Available constructors:

QuadraticTaylor(center, function)

Parameters
centersequence of float

Point \vect{x}_0 where the Taylor expansion of the function h is performed.

functionFunction

Function h to be approximated.

See also

LinearTaylor, LinearLeastSquares, QuadraticLeastSquares

Notes

The approximation of the model response \vect{y} = h(\vect{x}) around a specific set \vect{x}_0 = (x_{0,1},\dots,x_{0,n_{X}}) of input parameters may be of interest. One may then substitute h for its Taylor expansion at point \vect{x}_0. Hence h is replaced with a first or second-order polynomial \widehat{h} whose evaluation is inexpensive, allowing the analyst to apply the uncertainty anaysis methods.

We consider here the second order Taylor expansion around \ux=\vect{x}_0.

\vect{y} \, \approx \, \widehat{h}(\vect{x}) \, = \, h(\vect{x}_0) \, + \, \sum_{i=1}^{n_{X}} \; \frac{\partial h}{\partial x_i}(\vect{x}_0).\left(x_i - x_{0,i} \right) \, + \, \frac{1}{2} \; \sum_{i,j=1}^{n_X} \; \frac{\partial^2 h}{\partial x_i \partial x_j}(\vect{x}_0).\left(x_i - x_{0,i} \right).\left(x_j - x_{0,j} \right)

Introducing a vector notation, the previous equation rewrites:

\vect{y} \, \approx \, \vect{y}_0 \, + \, \vect{\vect{L}} \: \left(\vect{x}-\vect{x}_0\right) \, + \, \frac{1}{2} \; \left\langle \left\langle\vect{\vect{\vect{Q}}}\:, \vect{x}-\vect{x}_0 \right\rangle, \:\vect{x}-\vect{x}_0 \right\rangle

where

  • \vect{y_0} = (y_{0,1} , \dots, y_{0,n_Y})^{\textsf{T}} = h(\vect{x}_0) is the vector model response evaluated at \vect{x}_0 ;

  • \vect{x} is the current set of input parameters ;

  • \vect{\vect{L}} = \left( \frac{\partial y_{0,j}}{\partial x_i} \,,\, i=1,\ldots, n_X \,,\, j=1,\ldots, n_Y \right) is the transposed Jacobian matrix evaluated at \vect{x}_0 ;

  • \vect{\vect{Q}} = \left\{ \frac{\partial^2 y_{0,k}}{\partial x_i \partial x_j} \, \, , \, \, i,j=1,\ldots, n_X \, \, , \, \, k=1, \ldots, n_Y \right\} is the transposed Hessian matrix.

Examples

>>> import openturns as ot
>>> formulas = ['x1 * sin(x2)', 'cos(x1 + x2)', '(x2 + 1) * exp(x1 - 2 * x2)']
>>> myFunc = ot.SymbolicFunction(['x1', 'x2'], formulas)
>>> myTaylor = ot.QuadraticTaylor([1, 2], myFunc)
>>> myTaylor.run()
>>> responseSurface = myTaylor.getMetaModel()
>>> print(responseSurface([1.2,1.9]))
[1.13655,-0.999155,0.214084]

Methods

getCenter(self)

Get the center.

getClassName(self)

Accessor to the object’s name.

getConstant(self)

Get the constant vector of the approximation.

getId(self)

Accessor to the object’s id.

getInputFunction(self)

Get the function.

getLinear(self)

Get the gradient of the function at \vect{x}_0.

getMetaModel(self)

Get an approximation of the function.

getName(self)

Accessor to the object’s name.

getQuadratic(self)

Get the hessian of the function at \vect{x}_0.

getShadowedId(self)

Accessor to the object’s shadowed id.

getVisibility(self)

Accessor to the object’s visibility state.

hasName(self)

Test if the object is named.

hasVisibleName(self)

Test if the object has a distinguishable name.

run(self)

Perform the Quadratic Taylor expansion around \vect{x}_0.

setName(self, name)

Accessor to the object’s name.

setShadowedId(self, id)

Accessor to the object’s shadowed id.

setVisibility(self, visible)

Accessor to the object’s visibility state.
__init__(self, \*args)

Initialize self. See help(type(self)) for accurate signature.

getCenter(self)

Get the center.

Returns
centerPoint

Point \vect{x}_0 where the Taylor expansion of the function is performed.

getClassName(self)

Accessor to the object’s name.

Returns
class_namestr

The object class name (object.__class__.__name__).

getConstant(self)

Get the constant vector of the approximation.

Returns
constantVectorPoint

Constant vector of the approximation, equal to h(x_0).

getId(self)

Accessor to the object’s id.

Returns
idint

Internal unique identifier.

getInputFunction(self)

Get the function.

Returns
functionFunction

Function h to be approximated.

getLinear(self)

Get the gradient of the function at \vect{x}_0.

Returns
gradientMatrix

Gradient of the function h at the point \vect{x}_0 (the transposition of the jacobian matrix).

getMetaModel(self)

Get an approximation of the function.

Returns
approximationFunction

An approximation of the function h by a Quadratic Taylor expansion at the point \vect{x}_0.

getName(self)

Accessor to the object’s name.

Returns
namestr

The name of the object.

getQuadratic(self)

Get the hessian of the function at \vect{x}_0.

Returns
tensorSymmetricTensor

Hessian of the function h at the point \vect{x}_0.

getShadowedId(self)

Accessor to the object’s shadowed id.

Returns
idint

Internal unique identifier.

getVisibility(self)

Accessor to the object’s visibility state.

Returns
visiblebool

Visibility flag.

hasName(self)

Test if the object is named.

Returns
hasNamebool

True if the name is not empty.

hasVisibleName(self)

Test if the object has a distinguishable name.

Returns
hasVisibleNamebool

True if the name is not empty and not the default one.

run(self)

Perform the Quadratic Taylor expansion around \vect{x}_0.

setName(self, name)

Accessor to the object’s name.

Parameters
namestr

The name of the object.

setShadowedId(self, id)

Accessor to the object’s shadowed id.

Parameters
idint

Internal unique identifier.

setVisibility(self, visible)

Accessor to the object’s visibility state.

Parameters
visiblebool

Visibility flag.