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Software Tutorials
===================

Welcome to the PyTUQ Tutorials page! Here, you'll find a series of comprehensive tutorials designed to 
showcase the functionality of PyTUQ and guide you through its core features.

Surrogates
~~~~~~~~~~~

PyTUQ supports the construction of various surrogate models, including Polynomial Chaos Expansions for multivariate 
random variables and Neural Networks, which can be accessed through the Quantification of Uncertainties in Neural Networks (QUiNN) library.


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    <div class="sphx-glr-thumbcontainer" tooltip="This tutorial demonstrates how to create a Polynomial Chaos Expansion (PCE) surrogate model for scalar valued functions. We will use the pytuq.surrogates.pce wrapper class to approximate the function \sin^4(x).">

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  .. image:: /auto_examples/images/thumb/sphx_glr_ex_pce_thumb.png
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  :ref:`sphx_glr_auto_examples_ex_pce.py`

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      <div class="sphx-glr-thumbnail-title">Polynomial Chaos Expansion Construction</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to use the Neural Network wrapper class, pytuq.surrogates.nn with scalar valued functions. The constructor of the NN class accepts in an optional dictionary, net_options, to specify additional hyperparameters.  The build() and evaluate() functions similarly accept dictionaries and explicit keyword arguments during their respective function calls.">

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  .. image:: /auto_examples/images/thumb/sphx_glr_ex_nn_thumb.png
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  :ref:`sphx_glr_auto_examples_ex_nn.py`

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      <div class="sphx-glr-thumbnail-title">Residual Neural Network Construction</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="In this tutorial, we demonstrate how to approximate a function with sparse regression by constructing a Polynomial Chaos (PC) surrogate with  Bayesian compressive sensing (BCS). The function we will approximate here is the Genz Oscillatory function, defined as:">

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  .. image:: /auto_examples/images/thumb/sphx_glr_ex_genz_bcs_thumb.png
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  :ref:`sphx_glr_auto_examples_ex_genz_bcs.py`

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      <div class="sphx-glr-thumbnail-title">Function Approximation with Sparse Regression</div>
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.. toctree::
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   /auto_examples/ex_pce
   /auto_examples/ex_nn
   /auto_examples/ex_genz_bcs


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      :download:`Download all examples in Python source code: auto_examples_python.zip </auto_examples/auto_examples_python.zip>`

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      :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip </auto_examples/auto_examples_jupyter.zip>`


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    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_