Add model description.

README.md

@@ -4,6 +4,15 @@ My submission to the [**2023 FLAME AI Challenge**: *Flow Physics Crowd-science C
 The submission was ranked #10 on the [Kaggle leaderboard](https://www.kaggle.com/competitions/2023-flame-ai-challenge/leaderboard).
 
 
+## Model description
+
+The proposed model is a specialized convolutional neural network (CNN) designed for the task of upsampling low-resolution images.
+First, the model inputs of size 16x16x4 are passed through an upsampling layer with bilinear interpolation to increase the spatial resolution to 128x128x4 pixels.
+Subsequently, a sequence of convolutional layers processes the upsampled data to improve prediction accuracy with respect to the given the training data.
+We carefully optimized the hyper-parameters of the architecture and found that 4 layers mapping to 16 latent channels, together with a large kernel size of 15 and padding of 7, are a good trade-off between accuracy and training time.
+Notably, we added a skip connection around the convolutional layer block.
+The model is trained in two stages: First, we perform 10000 steps using Adam on a random batch of size 16 and, afterwards, fine-tune with LBFGS on batches of size 128 for another 1000 iterations.
+
 
 ## Content