diff --git a/README.md b/README.md
index a80a934b995c2fbcd2ca5ecc3785880a73e998ed..7170ecaee84c4fa462de08e42682ad221bc929fd 100644
--- a/README.md
+++ b/README.md
@@ -1,19 +1,29 @@
-# ECML-PKDD2021 CNN Boundary Conditions for spatio-temporal dynamics
+# Effects of Boundary Conditions in Fully Convolutional Networks for Learning Spatio-temporal Dynamics (ECML-PKDD 2021)
 
-This repository is the supplementary material of the article **"Effects of boundary conditions in fully convolutional networks for learning spatio-temporal dynamics"**, submitted to the Applied Data Science Tracks at ECML-PKDD 2021. It contains the complete description of the neural network and of the computing environement, used code, implementation details and supplementary results. 
+This repository contains the data, code and additional results of our [paper](https://arxiv.org/abs/2106.11160) accepted to the Applied Data Science Tracks at ECML-PKDD 2021. If you find this code useful in your research, please consider citing:
+    
+    @misc{alguacil2021effects,
+      title={Effects of boundary conditions in fully convolutional networks for learning spatio-temporal dynamics}, 
+      author={Antonio Alguacil and Wagner Gonçalves Pinto and Michael Bauerheim and Marc C. Jacob and Stéphane Moreau},
+      year={2021},
+      eprint={2106.11160},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG}
+    }
 
-Repository is organized as follows:
 
-- [images](./images): folder containing the figures shown in this page
-- [network](./network): implementation of the neural network, train and testing scripts
+The repository is organized as follows:
+
 - [data_generation](./data_generation): code for the generation of the database using Palabos
+- [network](./network): implementation of the neural network, train and testing scripts
 
-More details are availble in the subfolders
+You can browse the different subfolder to generate the data with an open-source CFD code, train the neural network or
+test the method.
 
 Network architecture
 ------------
 
-Neural network is multi-scale (field dimensions of N, N/2 and N/4), composed by 17 two-dimensional convolution operations, for a total of 422,419 trainable parameters. ReLUs are used as activation function and replication padding is used to maintain layers size unchanged inside each scale.
+The employed neural network is a Multi-Scale architecture [from this paper](https://arxiv.org/abs/1511.05440). 3 Scales are used, with dimensions N, N/2 and N/4, composed by 17 two-dimensional convolution operations, for a total of 422,419 trainable parameters. ReLUs are used as activation function and replication padding is used to maintain layers size unchanged inside each scale.
 
 <p align="center">
   <img alt="Neural network architecture" src="./images/drawing_network_architecture.png" width="800"/>
diff --git a/icml21_sm.code-workspace b/icml21_sm.code-workspace
deleted file mode 100644
index 876a1499c09dc083612f43c53c0ae71b9c30c5b1..0000000000000000000000000000000000000000
--- a/icml21_sm.code-workspace
+++ /dev/null
@@ -1,8 +0,0 @@
-{
-	"folders": [
-		{
-			"path": "."
-		}
-	],
-	"settings": {}
-}
\ No newline at end of file