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ot-smt documentation
====================

.. image:: _static/krg_Test_test_krg.png
     :align: left
     :scale: 50%
	 
otSMT is a module of OpenTURNS implementing some methods to bind surrogate models from `SMT <https://smt.readthedocs.io/en/latest/>`_ into OpenTURNS PythonFunctions.

Available surrogate models from SMT: 

- Least Squares Model
- Neural Network Model
- Radial Basis Function
- Inverse Distance Weighting
- Regularized minimal-energy tensor-product splines
- Second-order polynomial approximation
- Kriging
- Kriging Partial Least Squares (KPLS)
- KPLSK
- Gradient Enhanced KPLS
- Mixtures of Experts

Available multifidelity surrogate models from SMT:

- Multi-Fidelity Kriging 
- Multi-Fidelity KPLS   
- Multi-Fidelity KPLSK  

Available mixed-variables surrogate models from SMT: 

- Mixed Integer Kriging with Continuous Relaxation
- Mixed Integer Kriging with Gower Distance

Documentation about SMT can be found `here <https://smt.readthedocs.io/en/latest/>`_

User documentation
------------------

.. toctree::
   :maxdepth: 1  
   
   user_manual/user_manual



Examples 
--------

.. toctree::
   :maxdepth: 2  
   
   examples/examples


References
----------
- Bouhlel, M. A., Hwang, J. T., Bartoli, N., Lafage, R., Morlier, J., & Martins, J. R. (2019). A Python surrogate modeling framework with derivatives. Advances in Engineering Software, 135, 102662.

Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`