Taylor approximationsΒΆ

In this example we are going to build a local approximation of a model using the Taylor decomposition:

Here is the decomposition at the first order:

\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)

Here h(x) = [cos(x_1 + x_2), (x_2 + 1) e^{x_1 - 2 x_2}].

import openturns as ot
import openturns.viewer as viewer
from matplotlib import pylab as plt
ot.Log.Show(ot.Log.NONE)

# Prepare some data.
formulas = ['cos(x1 + x2)', '(x2 + 1) * exp(x1 - 2 * x2)']
model = ot.SymbolicFunction(['x1', 'x2'], formulas)

# Center of the approximation.
x0 = [-0.4, -0.4]

# Drawing bounds.
a = -0.4
b = 0.0

Create a linear (first-order) Taylor approximation.

algo = ot.LinearTaylor(x0, model)
algo.run()
responseSurface = algo.getMetaModel()

Plot the second output of our model with x_1=x_{0,1}.

graph = ot.ParametricFunction(
    responseSurface, [0], [x0[1]]).getMarginal(1).draw(a, b)
graph.setLegends(['taylor'])
curve = ot.ParametricFunction(model, [0], [x0[1]]).getMarginal(
    1).draw(a, b).getDrawable(0)
curve.setColor('red')
curve.setLegend('model')
graph.add(curve)
graph.setLegendPosition('topright')
view = viewer.View(graph)
y1 as a function of x2

Here is the decomposition at the second order:

Create a quadratic (second-order) Taylor approximation.

algo = ot.QuadraticTaylor(x0, model)
algo.run()
responseSurface = algo.getMetaModel()

Plot second output of our model with x_1=x_{0,1}.

graph = ot.ParametricFunction(
    responseSurface, [0], [x0[1]]).getMarginal(1).draw(a, b)
graph.setLegends(['taylor'])
curve = ot.ParametricFunction(model, [0], [x0[1]]).getMarginal(
    1).draw(a, b).getDrawable(0)
curve.setColor('red')
curve.setLegend('model')
graph.add(curve)
graph.setLegendPosition('topright')
view = viewer.View(graph)
plt.show()
y1 as a function of x2

Total running time of the script: ( 0 minutes 0.143 seconds)

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