Metamodel of a field function

In this example we are going to create a metamodel of a field function following these steps:

1. Creation of a field model over an 1-d mesh.

2. Creation of a Gaussian process.

3. Karhunen-Loeve decomposition of a process with known covariance function.

4. Karhunen-Loeve decomposition of a process with known trajectories.

5. Projection of fields.

6. Functional chaos decomposition between the coefficients of the input and output processes.

7. Build a metamodel of the whole field model.

8. Validate the metamodel.

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

Input model

print("Create the input process")
# Domain bound
a = 1
# Reference correlation length
b = 0.5
# Number of vertices in the mesh
N = 100
# Bandwidth of the smoothers
h = 0.05

mesh = ot.IntervalMesher([N - 1]).build(ot.Interval(-a, a))
covariance_X = ot.AbsoluteExponential([b])
process_X = ot.GaussianProcess(covariance_X, mesh)

Out:

Create the input process

for some pretty graphs

def drawKL(scaledKL, KLev, mesh, title="Scaled KL modes"):
    graph_modes = scaledKL.drawMarginal()
    graph_modes.setTitle(title + " scaled KL modes")
    graph_modes.setXTitle('$x$')
    graph_modes.setYTitle(r'$\sqrt{\lambda_i}\phi_i$')
    data_ev = [[i, KLev[i]] for i in range(scaledKL.getSize())]
    graph_ev = ot.Graph()
    graph_ev.add(ot.Curve(data_ev))
    graph_ev.add(ot.Cloud(data_ev))
    graph_ev.setTitle(title + " KL eigenvalues")
    graph_ev.setXTitle('$k$')
    graph_ev.setYTitle(r'$\lambda_i$')
    graph_ev.setAxes(True)
    graph_ev.setGrid(True)
    graph_ev.setLogScale(2)
    bb = graph_ev.getBoundingBox()
    lower = bb.getLowerBound()
    lower[1] = 1.0e-7
    bb = ot.Interval(lower, bb.getUpperBound())
    graph_ev.setBoundingBox(bb)
    return graph_modes, graph_ev

Karhunen-Loeve decomposition of the input process

print("Compute the decomposition of the input process")
threshold = 0.0001
algo_X = ot.KarhunenLoeveP1Algorithm(
    mesh, process_X.getCovarianceModel(), threshold)
algo_X.run()
result_X = algo_X.getResult()
phi_X = result_X.getScaledModesAsProcessSample()
lambda_X = result_X.getEigenvalues()

graph_modes_X, graph_ev_X = drawKL(phi_X, lambda_X, mesh, "X")
view = viewer.View(graph_modes_X)

Out:

Compute the decomposition of the input process

Input database generation

print("Sample the input process")
size = 1000
sample_X = process_X.getSample(size)

Out:

Sample the input process

The field model: convolution over an 1-d mesh

class ConvolutionP1(ot.OpenTURNSPythonFieldFunction):
    def __init__(self, p, mesh):
        # 1 = input dimension, the dimension of the input field
        # 1 = output dimension, the dimension of the output field
        # 1 = mesh dimension
        super(ConvolutionP1, self).__init__(mesh, 1, mesh, 1)
        # Here we define some constants and we set-up the invariant part of the execution
        self.setInputDescription(["x"])
        self.setOutputDescription(["y"])
        vertices = mesh.getVertices()
        size = vertices.getSize()
        self.mat_W_ = ot.SquareMatrix(size)
        for i in range(size):
            x_minus_t = (vertices - vertices[i]) * (-1.0)
            values_w = p(x_minus_t)
            for j in range(size):
                self.mat_W_[i, j] = values_w[j, 0]
        G = mesh.computeP1Gram()
        self.mat_W_ = self.mat_W_ * G

    def _exec(self, X):
        point_X = [val[0] for val in X]
        values_Y = self.mat_W_ * point_X
        return [[v] for v in values_Y]

Dynamical model: convolution wrt kernel p

print("Create the convolution function")
p = ot.SymbolicFunction("x", "exp(-(x/" + str(h) + ")^2)")
myConvolution = ot.FieldFunction(ConvolutionP1(p, mesh))

Out:

Create the convolution function

Output database generation

print("Sample the output process")
sample_Y = myConvolution(sample_X)

Out:

Sample the output process

Karhunen-Loeve decomposition of the output process

print("Compute the decomposition of the output process")
algo_Y = ot.KarhunenLoeveSVDAlgorithm(sample_Y, threshold)
algo_Y.run()
result_Y = algo_Y.getResult()
phi_Y = result_Y.getScaledModesAsProcessSample()
lambda_Y = result_Y.getEigenvalues()
graph_modes_Y, graph_ev_Y = drawKL(phi_Y, lambda_Y, mesh, "Y")
view = viewer.View(graph_modes_Y)

Out:

Compute the decomposition of the output process

Compare eigenvalues of X and Y

graph_ev_X.add(graph_ev_Y)
graph_ev_X.setTitle("Input/output eigenvalues comparison")
graph_ev_X.setYTitle(r"$\lambda_X, \lambda_Y$")
graph_ev_X.setColors(["blue", "blue", "red", "red"])
graph_ev_X.setLegends([r"$\lambda_X$", "", r"$\lambda_Y$", ""])
graph_ev_X.setLegendPosition("topright")
view = viewer.View(graph_ev_X)

Polynomial chaos between KL coefficients

print("project sample_X")
sample_xi_X = result_X.project(sample_X)

print("project sample_Y")
sample_xi_Y = result_Y.project(sample_Y)

print("Compute PCE between coefficients")
degree = 1
dimension_xi_X = sample_xi_X.getDimension()
dimension_xi_Y = sample_xi_Y.getDimension()
enumerateFunction = ot.LinearEnumerateFunction(dimension_xi_X)
basis = ot.OrthogonalProductPolynomialFactory(
    [ot.HermiteFactory()] * dimension_xi_X, enumerateFunction)
basisSize = enumerateFunction.getStrataCumulatedCardinal(degree)
adaptive = ot.FixedStrategy(basis, basisSize)
projection = ot.LeastSquaresStrategy(
    ot.LeastSquaresMetaModelSelectionFactory(ot.LARS(), ot.CorrectedLeaveOneOut()))
ot.ResourceMap.SetAsScalar(
    "LeastSquaresMetaModelSelection-ErrorThreshold", 1.0e-7)
algo_chaos = ot.FunctionalChaosAlgorithm(
    sample_xi_X, sample_xi_Y, basis.getMeasure(), adaptive, projection)
algo_chaos.run()
result_chaos = algo_chaos.getResult()
meta_model = result_chaos.getMetaModel()
print("myConvolution=", myConvolution.getInputDimension(),
      "->", myConvolution.getOutputDimension())
preprocessing = ot.KarhunenLoeveProjection(result_X)
print("preprocessing=", preprocessing.getInputDimension(),
      "->", preprocessing.getOutputDimension())
print("meta_model=", meta_model.getInputDimension(),
      "->", meta_model.getOutputDimension())
postprocessing = ot.KarhunenLoeveLifting(result_Y)
print("postprocessing=", postprocessing.getInputDimension(),
      "->", postprocessing.getOutputDimension())
meta_model_field = ot.FieldToFieldConnection(
    postprocessing, ot.FieldToPointConnection(meta_model, preprocessing))

Out:

project sample_X
project sample_Y
Compute PCE between coefficients
myConvolution= 1 -> 1
preprocessing= 1 -> 99
meta_model= 99 -> 33
postprocessing= 33 -> 1

Meta_model validation

iMax = 10
sample_X_validation = process_X.getSample(iMax)
sample_Y_validation = myConvolution(sample_X_validation)

graph_sample_Y_validation = sample_Y_validation.drawMarginal(0)
sample_Y_hat = meta_model_field(sample_X_validation)
graph = sample_Y_hat.drawMarginal(0)
for i in range(iMax):
    dr = graph.getDrawable(i)
    dr.setLineStyle("dashed")
    graph_sample_Y_validation.add(dr)
graph_sample_Y_validation.setTitle(r"Comparison $Y_i$ and $\tilde{Y}_i$")
graph_sample_Y_validation.setXTitle(r"$t$")
graph_sample_Y_validation.setYTitle(r"$Y$, $\tilde{Y}$")
view = viewer.View(graph_sample_Y_validation)

graph_sample_X = sample_X_validation.drawMarginal(0)
graph_sample_X.setTitle(r"Trajectory $X$")
graph_sample_X.setXTitle(r"$t$")
graph_sample_X.setYTitle(r"$X$")
view = viewer.View(graph_sample_X)

graph_sample_Y = sample_Y_validation.drawMarginal(0)
graph_sample_Y.setTitle(r"Trajectory $Y$")
graph_sample_Y.setXTitle(r"$t$")
graph_sample_Y.setYTitle(r"$Y$")
view = viewer.View(graph_sample_Y)
plt.show()