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.. _sphx_glr_auto_introduction_plot_introduction_ua.py:


Introduction to uncertainty analysis
====================================

Typically, we have some model, which depends on parameters, e.g., :math:`\tau_{e,i}, \tau_{e,i}, ..., C_i`
and we are interested in several quantities of interest (QOI), e.g., :math:`y_j`, we can compute, when we
run the model with the desired set of parameters.

.. image:: /examples/images/Uncertainty_Analysis.png
    :width: 700
    :align: center

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Uncertainty analysis using Monte Carlo methods
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In order to determine the statistical information of the output quantities of a system, such as the mean
or the variance, one of the most common methods is Monte Carlo sampling (MC). The MC method is based on
repetitive calculations of the forward model, while defining the random inputs according to their
probability distributions. This results in an ensemble of solutions from which statistical properties
can be derived. However, a large number of simulations is needed due to the slow convergence rate of the
MC method. The mean for example converges with :math:`1/\sqrt{N}`, where $N$ is the number of deterministic
forward calculations. This limits the applicability to problems with low computational cost.

Hence, the whole UQ problem reduces to find the *unknown* transfer function (grey box) from which
we can infer all necessary information (statistics, sensitivities, etc)

Example
^^^^^^^
Three-dimensional test function of Ishigami.

.. math::
    y = \sin(x_1) + a \sin(x_2)^2 + b x_3^4 \sin(x_1)

The Ishigami function of Ishigami & Homma (1990) is used as an example for uncertainty
and sensitivity analysis methods, because it exhibits strong nonlinearity and nonmonotonicity.
It also has a peculiar dependence on :math:`x_3`, as described by Sobol and Levitan (1999).
The values of a and b used by Crestaux et al. (2007) and Marrel et al. (2009) are: a = 7 and b = 0.1.

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.. code-block:: default


    import pygpc
    import numpy as np
    import matplotlib.pyplot as plt
    from mpl_toolkits.mplot3d import Axes3D

    # Parameters
    p = dict()
    p["x1"] = (np.random.rand(int(1e5))-0.5)*2*np.pi
    p["x2"] = (np.random.rand(int(1e5))-0.5)*2*np.pi
    p["x3"] = np.zeros(p["x1"].shape)
    p["a"] = 7
    p["b"] = 0.1

    # Model
    model = pygpc.testfunctions.Ishigami().set_parameters(p)

    # Run simulations
    y = model.simulate()

    # Plot results
    fig = plt.figure(figsize=[15, 4])

    N = np.logspace(1, 5, 5).astype(int)
    mean = []
    std = []

    for i in range(len(N)):
        mean.append(np.mean(y[:N[i]]))
        std.append(np.std(y[:N[i]]))

    ax1 = fig.add_subplot(131, projection='3d')
    ax1.scatter(p["x1"][:N[-1]],
                p["x2"][:N[-1]],
                y[:N[-1]],
                s=4, c=y[0:N[-1]].flatten(), cmap="jet")
    ax1.set_xlabel("x1")
    ax1.set_ylabel("x2")
    ax1.set_zlabel("y")
    ax1.view_init(elev=45, azim=180)

    ax2 = fig.add_subplot(132)
    ax2.plot(N, mean)
    ax2.set_xscale("log")
    ax2.grid(True)
    ax2.set_xlabel("N")
    ax2.set_ylabel("Mean")

    ax3 = fig.add_subplot(133)
    ax3.plot(N, std)
    ax3.set_xscale("log")
    ax3.grid(True)
    ax3.set_xlabel("N")
    _ = ax3.set_ylabel("STD")




.. image-sg:: /auto_introduction/images/sphx_glr_plot_introduction_ua_001.png
   :alt: plot introduction ua
   :srcset: /auto_introduction/images/sphx_glr_plot_introduction_ua_001.png
   :class: sphx-glr-single-img





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It can be clearly seen that the mean and the standard deviation converge slowly with increasing :math:`N`,
demonstrating the bad convergence properties of standard Monte Carlo approaches.

References
^^^^^^^^^^
.. [1] Ishigami, T., Homma, T. (1990, December). An importance quantification
   technique in uncertainty analysis for computer models. In Uncertainty
   Modeling and Analysis, 1990. Proceedings., First International Symposium
   on (pp. 398-403). IEEE.

.. [2] Sobol', I.M., Levitan, Y.L. (1999). On the use of variance reducing
   multipliers in Monte Carlo computations of a global sensitivity index.
   Computer Physics Communications, 117(1), 52-61.

.. [3] Crestaux, T., Martinez, J.-M., Le Maitre, O., & Lafitte, O. (2007).
   Polynomial chaos expansion for uncertainties quantification and sensitivity analysis [PowerPoint slides].
   Retrieved from SAMO 2007 website: http://samo2007.chem.elte.hu/lectures/Crestaux.pdf.

.. [4] Marrel, A., Iooss, B., Laurent, B., & Roustant, O. (2009).
   Calculations of sobol indices for the gaussian process metamodel.
   Reliability Engineering & System Safety, 94(3), 742-751.


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