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Continuous PDE Dynamics Forecasting with Implicit Neural Representations

Official PyTorch implementation of DINo (Dynamics-aware Implicit Neural Representation) | Accepted at ICLR 2023 (Notable-Top-25%, Spotlight) | Arxiv

Yuan Yin (equal contribution), Matthieu Kirchmeyer (equal contribution), Jean-Yves Franceschi (equal contribution), Alain Rakotomamonjy, Patrick Gallinari

shallow_water_twitter.mp4
navier_stokes_twitter.mp4

The requirements.txt file lists Python package dependencies.

Data

  • For navier_stokes, wave, data is generated as part of our script c.f. data_pdes.py.
  • For shallow_water, data can be found here. It should be stored in ./results/shallow_water or in a custom location given as argument to -f.

Pretrained models

We provide the checkpoints here

  • NS_100 for navier_stokes (100% subsampling rate)
  • Wave_100 for wave (100% subsampling rate)
  • SW for shallow_water (c.f. our paper)

They should be stored in ./results/<DATASET> and can be used for:

  • warm start via train.py; the path of the model should be given as argument via -c.
  • inference via test.py.

We do not control the behavior of the checkpoints on other datasets than those generated by our code.

Training

python3 train.py -d <DATASET> -g 0 -r <RATE>
  • -c: checkpoint location for warm-start with a pretrained model (default: no warmstart)
  • -d: input dataset (navier_stokes, wave, shallow_water)
  • -f: home path (defaults to "./results")
  • -g: gpu id (defaults to 0)
  • -r: subsampling rate (defaults to 1.0)
  • -w: the length of conditioning window for non-Markovian dynamics (defaults to Markovian - 0)

By running the train script, it will generate an unique id of each run, called the run_id.

Logs are available in ./results/<RUN_ID>/log and display the result over In/Out-s + In/Out-t for both train and test trajectories (cf the Figure below where red represents observed data and our paper for more details). These are the numbers reported in Table 2.

task

Inference on new conditions

The run_id can also be used to run the inference script on test trajectories for the following settings:

  • Evaluation on a new grid for navier_stokes, wave (Table 3.a).
  • Super-resolution for shallow_water (Figure 5).
python3 test.py -d <DATASET> -p <RUN_ID> -g 0 -r <RATE> -s <SEED>
  • -d: input dataset (navier_stokes, wave, shallow_water_hr)
  • -f: home path (defaults to "./results")
  • -g: gpu id (defaults to 0)
  • -p: run id
  • -r: subsampling rate (defaults to 1.0)
  • -s: subsampling seed (defaults to 1 the train subsampling seed; for Table 3.a. we chose -s 2)
dino_twitter_crop.mp4

Citation

@inproceedings{Yin2023,
title={Continuous PDE Dynamics Forecasting with Implicit Neural Representations},
author={Yin, Yuan and Kirchmeyer, Matthieu and Franceschi, Jean-Yves and Rakotomamonjy, Alain and Gallinari, Patrick},
booktitle={International Conference on Learning Representations},
year={2023},
url={https://openreview.net/forum?id=B73niNjbPs}
}