https://github.com/mkirchmeyer/dino
Time- and space-continuous neural PDE forecaster based on INRs and ODEs
https://github.com/mkirchmeyer/dino
dynamical-systems implicit-neural-representation pde
Last synced: 10 months ago
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Time- and space-continuous neural PDE forecaster based on INRs and ODEs
- Host: GitHub
- URL: https://github.com/mkirchmeyer/dino
- Owner: mkirchmeyer
- License: apache-2.0
- Created: 2022-10-05T15:15:51.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-01-13T17:33:06.000Z (over 2 years ago)
- Last Synced: 2024-01-14T02:58:17.607Z (over 2 years ago)
- Topics: dynamical-systems, implicit-neural-representation, pde
- Language: Python
- Homepage: https://arxiv.org/abs/2209.14855
- Size: 59.6 KB
- Stars: 36
- Watchers: 4
- Forks: 7
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
README
# 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)](https://openreview.net/forum?id=B73niNjbPs) | [Arxiv](https://arxiv.org/abs/2209.14855)
[Yuan Yin](https://yuan-yin.github.io/) (equal contribution), [Matthieu Kirchmeyer](https://mkirchmeyer.github.io/) (equal contribution), [Jean-Yves Franceschi](https://jyfranceschi.fr) (equal contribution), [Alain Rakotomamonjy](http://asi.insa-rouen.fr/enseignants/~arakoto/), [Patrick Gallinari](http://www-connex.lip6.fr/~gallinar/gallinari/pmwiki.php)
https://user-images.githubusercontent.com/15007187/194546732-dafb7627-4be3-4e8e-910a-9b08ad375158.mp4
https://user-images.githubusercontent.com/15007187/194546967-557280ff-bde0-4bb4-9650-c9ec1f7bf986.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](https://doi.org/10.6084/m9.figshare.21298179).
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](https://doi.org/10.6084/m9.figshare.21298251)
* `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/` 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 -g 0 -r
```
* `-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//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.
## 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 -p -g 0 -r -s
```
* `-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`)
https://user-images.githubusercontent.com/15007187/194547306-e0f151cf-a4fc-43be-a907-35816124020d.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}
}
```