Merge pull request SMTorg#163 from bouhlelma/master

Add ref paper in the doc
alpaan · Jul 19, 2019 · 62c3f2c · 62c3f2c
2 parents b0feeae + bcb42b5
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diff --git a/doc/_src_docs/surrogate_models/gekpls.rst b/doc/_src_docs/surrogate_models/gekpls.rst
diff --git a/doc/_src_docs/surrogate_models/gekpls.rstx b/doc/_src_docs/surrogate_models/gekpls.rstx
@@ -21,12 +21,10 @@ This approach reduces the number of hyperparameters (reduced dimension) from :ma
 As previously mentioned, PLS is applied several times with respect to each sampling point, which provides the influence of each input variable around that point.
 The idea here is to add only m approximating points :math:`(m \in [1, nx])` around each sampling point.
 Only the :math:`m` highest coefficients given by the first principal component are considered, which usually contains the most useful information.
-More details of such approach are given in [2]_.
+More details of such approach are given `here <http://mdolab.engin.umich.edu/content/gradient-enhanced-kriging-high-dimensional-problems>`_.
 
 .. [1] Forrester, I. J. and Sobester, A. and Keane, A. J., Engineering Design via Surrogate Modeling: A Practical Guide. Wiley, 2008 (Chapter 7).
 
-.. [2] Bouhlel, M. A. and Martins, J. R. R. A., Gradient-enhanced kriging for high-dimensional problems (under review), Engineering with Computers, 2017.
-
 Usage
 -----
 

diff --git a/doc/_src_docs/surrogate_models/rmts.rst b/doc/_src_docs/surrogate_models/rmts.rst
diff --git a/doc/_src_docs/surrogate_models/rmts.rstx b/doc/_src_docs/surrogate_models/rmts.rstx
@@ -85,6 +85,7 @@ The number of elements in each dimension is an option that trades off efficiency
 
 In general, RMTB is the better choice when training time is the most important,
 while RMTC is the better choice when accuracy of the interpolant is the most important.
+More details of these methods are given `here <http://mdolab.engin.umich.edu/content/fast-prediction-surrogate-model-large-datasets>`_.
 
 Usage (RMTB)
 ------------

diff --git a/doc/index.rst b/doc/index.rst
diff --git a/doc/index.rstx b/doc/index.rstx
@@ -11,7 +11,22 @@ SMT is designed to make it easy for developers to implement new surrogate models
 
 The code is available open-source on `GitHub <https://github.com/SMTorg/SMT>`_.
 
-To cite SMT: M. A. Bouhlel and J. T. Hwang and N. Bartoli and R. Lafage and J. Morlier and J. R. R. A. Martins. A Python surrogate modeling framework with derivatives. Advances in Engineering Software, 2019.
+Cite us
+-------
+To cite SMT: M. A. Bouhlel and J. T. Hwang and N. Bartoli and R. Lafage and J. Morlier and J. R. R. A. Martins.  `A Python surrogate modeling framework with derivatives. Advances in Engineering Software, 2019 <http://mdolab.engin.umich.edu/content/python-surrogate-modeling-framework-derivatives>`_. 
+
+.. code-block:: none
+
+	@article{SMT2019,
+		Author = {Mohamed Amine Bouhlel and John T. Hwang and Nathalie Bartoli and Rémi Lafage and Joseph Morlier and Joaquim R. R. A. Martins},
+		Journal = {Advances in Engineering Software},
+		Title = {A Python surrogate modeling framework with derivatives},
+		pages = {102662},
+		year = {2019},
+		issn = {0965-9978},
+		doi = {https://doi.org/10.1016/j.advengsoft.2019.03.005},
+		Year = {2019}}
+
 
 Focus on derivatives
 --------------------