Merge pull request #362 from SciML/bayesian_koopman

GPU accelerated Bayesian Koopman

README.md

@@ -5,8 +5,8 @@
 [![Join the chat at https://gitter.im/JuliaDiffEq/Lobby](https://badges.gitter.im/JuliaDiffEq/Lobby.svg)](https://gitter.im/JuliaDiffEq/Lobby?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
 
 SciMLTutorials.jl holds PDFs, webpages, and interactive Jupyter notebooks
-showing how to utilize the software in the [SciML Scientific Machine Learning ecosystem](https://sciml.ai/). 
-This set of tutorials was made to complement the [documentation](https://sciml.ai/documentation/) 
+showing how to utilize the software in the [SciML Scientific Machine Learning ecosystem](https://sciml.ai/).
+This set of tutorials was made to complement the [documentation](https://sciml.ai/documentation/)
 and the [devdocs](http://devdocs.sciml.ai/latest/)
 by providing practical examples of the concepts. For more details, please
 consult the docs.
@@ -60,11 +60,12 @@ SciMLTutorials.open_notebooks()
 - DiffEqUncertainty
   - [An Intro to Expectations via DiffEqUncertainty.jl](http://tutorials.juliadiffeq.org/html/DiffEqUncertainty/01-expectation_introduction.html)
   - [Optimization Under Uncertainty with DiffEqUncertainty.jl](http://tutorials.juliadiffeq.org/html/DiffEqUncertainty/02-AD_and_optimization.html)
+  - [GPU-Accelerated Data-Driven Bayesian Uncertainty Quantification with Koopman Operators](http://tutorials.juliadiffeq.org/html/DiffEqUncertainty/03-GPU_Bayesian_Koopman.html)
 - Advanced
   - [A 2D Cardiac Electrophysiology Model (CUDA-accelerated PDE solver)](http://tutorials.juliadiffeq.org/html/advanced/01-beeler_reuter.html)
   - [Solving Stiff Equations](http://tutorials.juliadiffeq.org/html/advanced/02-advanced_ODE_solving.html)
   - [Kolmogorov Backward Equations](http://tutorials.juliadiffeq.org/html/advanced/03-kolmogorov_equations.html)
-  
+
 ## Contributing
 
 First of all, make sure that your current directory is `SciMLTutorials`. All