My submission to the 2023 FLAME AI Challenge: Flow Physics Crowd-science Challenge for Turbulent Super-resolution. The submission was ranked #10 on the Kaggle leaderboard.
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.
Training script: flame.py
Model parameters: working/model.pt
Submission file: working/submission.csv
Requirements: torch, numpy, pandas, matplotlib.
Train the model by running python flame.py.
If you want run the script with trained model weights, set load_model_weights = True in flame.py.
