The code are for the paper
PDE-Net: Learning PDEs from Data(ICML 2018)
Long Zichao, Lu Yiping, Ma Xianzhong and Dong Bin
If you find the code useful in your research then please cite
@inproceedings{long2018pde,
title={PDE-Net: Learning PDEs from Data},
author={Long, Zichao and Lu, Yiping and Ma, Xianzhong and Dong, Bin},
booktitle={International Conference on Machine Learning},
pages={3214--3222},
year={2018}
}
All code were developed and tested on CentOS 7 with Python 3.6, and were implemented by pytorch 0.3.1
The code are based on a submodule: aTEAM, a pyTorch Extension for Applied Mathematics. One can either download a proper version of aTEAM and extract it to some directory in your python path with name 'aTEAM', or use 'git submodule' to set up the dependency.
For example, you can create a conda environment and set up PDE-Net like this:
conda create -n PDE-Net python=3.6 scipy pyyaml jupyter matplotlib
source activate PDE-Net
conda install pytorch=0.3.1 torchvision cuda90 -c pytorch
git clone [email protected]:ZichaoLong/PDE-Net.git
cd PDE-Net
git checkout PDE-Net # switch your branch from 'master' to the 'PDE-Net'
git submodule init
git submodule update
| Model | example of config file | training | testing | plot |
|---|---|---|---|---|
| Convection-Diffusion Equations | checkpoint/linpde.yaml | learn_variantcoelinear2d.py | linpdetest.py | linpdeplot.py |
| Diffusion Equations with Nonlinear Source | checkpoint/nonlinpde.yaml | learn_singlenonlinear2d.py | nonlinpdetest.py | nonlinpdeplot.py |
- Default options can be found in learn_variantcoelinear.py and learn_singlenonlinear2d.py. You can simply modify the default options in learn_variantcoelinear.py(learn_singlenonlinear2d.py), and simply run code like:
python learn_variantcoelinear2d.py
- Configure training by command line options:
TASKDESCRIPTOR=linpde-test
python learn_variantcoelinear2d.py --taskdescriptor=$TASKDESCRIPTOR \
--kernel_size=7 --max_order=4 --constraint=moment
Training information and learned parameters will be stored in checkpoint/${TASKDESCRIPTOR}.
python linpdetest.py $TASKDESCRIPTOR
# or python nonlinpdetest.py $TASKDESCRIPTOR
Then the testing results will be stored in checkpoint/$TASKDESCRIPTOR/errs.pkl.
Set your TASKDESCRIPTOR in *test.py, *plot.py, errs_compare.py and run.
Download pretrained models and make your working directory like this:
PDE-Net/
aTEAM/
figures/
learn_variantcoelinear2d.py
linpdetest.py
...
checkpoint/
linpde5x5frozen4order0.015dt0.015noise-double/
linpde5x5moment4order0.015dt0.015noise-double/
linpde7x7frozen4order0.015dt0.015noise-double/
linpde7x7moment4order0.015dt0.015noise-double/
nonlinpde7x7frozen2order-double/
nonlinpde7x7moment2order-double/
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