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[NeurIPS 2023] A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting

Home Page: https://salvarc.github.io/blog/2023/dyffusion

License: Apache License 2.0

Makefile 0.36% Python 94.94% Shell 1.87% C++ 0.31% Cuda 2.51%
deep-learning diffusion diffusion-models ensemble-forecasts machine-learning neurips neurips-2023 probabilistic-forecasting pytorch pytorch-lightning spatiotemporal-forecasting

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dyffusion's Issues

Some Difference in Reproduced Results

The paper and the code are both very well done and have been very helpful for my research. I particularly appreciated the detailed explanations and the thoroughness of the experiments.
I cloned this repository and trained the dyffusion, but I got some difference in reproduced results.
Steps Followed
I followed the steps provided in the README to train the model:
First stage: Train the interpolator network
python run.py experiment=spring_mesh_interpolation
Second stage: Train the forecaster network
Encountered some issues, so I commented out lines 229-231 in wandb_api.py:
if os.path.exists(ckpt_path) and ckpt_path != best_model_fname: os.remove(ckpt_path) # remove if one exists from before os.rename(best_model_fname, ckpt_path)
Then ran:
python run.py experiment=spring_mesh_dyffusion diffusion.interpolator_run_id=<WANDB_RUN_ID>
Testing:
python run.py mode=test logger.wandb.project=DYffusion-spring-mesh logger.wandb.id=<run_id>

Actual Results
test/50ens_mems/avg/crps = 1.54671955108642
test/50ens_mems/avg/mse = 36.7373008728027
test/50ens_mems/avg/ssr = 2.02742147445678

Results on Navier Stokes Dataset
On the Navier Stokes dataset, I was able to reproduce results similar to those reported in your paper.
Could you please provide guidance on what might be causing these discrepancies? Any help or suggestions would be greatly appreciated. Thank you

How long was the training time roughly?

Hello, very great work. I am very interested in your work. May I ask on what GPU your experiments are trained? And how long was the training time roughly?

The baseline reproduction result is wrong

OISST dataset:
DDPM test results for SST:
test/50ens_mems/avg/mse=1211.059
test/50ens_mems/avg/crps=24.399
test/50ens_mems/avg/ssr=2.479
The results of the two repeated experiments are almost the same
But for Dyffusion and MCVD, I can reproduce the results similar to those in the paper according to your code
I would like to ask how to reproduce the results of DDPM
Secondly, do you have the corresponding codes for the other two baselines Dropout and Perturbation in your project?
I am a beginner and hope to get your help.

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