Merge pull request #21 from NREL/gb/doi_release

incremented veresion for doi release

README.rst

@@ -42,9 +42,9 @@ applications, such as when machine learning models produce physically
 inconsistent results or have trouble generalizing to out-of-sample scenarios.
 
 For details on the phygnn class framework see `the phygnn module documentation
-here. <https://nrel.github.io/phygnn/phygnn/phygnn.phygnn.html>`_ 
+here. <https://nrel.github.io/phygnn/phygnn/phygnn.phygnn.html>`_
 
-For example notebooks using the phygnn architecture for regression, 
+For example notebooks using the phygnn architecture for regression,
 classification, and even GAN applications, see `the example notebooks here.
 <https://github.com/NREL/phygnn/tree/master/examples>`_
 
@@ -75,9 +75,12 @@ helped inspire this work:
     * Anuj Karpatne, Gowtham Atluri, James H Faghmous, Michael Steinbach, Arindam Banerjee, Auroop Ganguly, Shashi Shekhar, Nagiza Samatova, and Vipin Kumar. 2017. Theory-guided data science: A new paradigm for scientific discovery from data. IEEE Transactions on knowledge and data engineering 29, 10 (2017), 2318–2331.
     * Justin Sirignano, Jonathan F. MacArt, Jonathan B. Freund, "DPM: A deep learning PDE augmentation method with application to large-eddy simulation". Journal of Computational Physics, Volume 423, 2020, ISSN 0021-9991, https://doi.org/10.1016/j.jcp.2020.109811.
 
-Suggested citation if you use the phygnn architecture:
+Suggested Citation
+==================
+
+
+Buster G, Rossol M, Bannister M, and Hettinger D, “physics-guided neural networks (phygnn).” GitHub. https://github.com/NREL/phygnn. Version 0.0.7. Accessed 14 January 2021
 
-    * Buster G, Rossol M, Bannister M, and Hettinger D, “physics-guided neural networks (phygnn).” GitHub. https://github.com/NREL/phygnn. Version 0.0.7. Accessed 14 January 2021
 
 Installation
 ============
@@ -109,8 +112,8 @@ Developer Install
 
     i. Start ipython and test the following import: ``from phygnn import PhysicsGuidedNeuralNetwork``
     ii. Navigate to the ``tests/`` directory and run the command: ``pytest``
-    
-    
+
+
 Acknowledgements
 ================
 This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy,

phygnn/version.py

@@ -1,4 +1,4 @@
 # -*- coding: utf-8 -*-
 """Physics Guided Neural Network version."""
 
-__version__ = '0.0.8'
+__version__ = '0.0.9'