โ ๏ธ You can now use a unify api for neural operator at https://github.com/neuraloperator/neuraloperator: you shouldn"t use this repo now !
The original code and package come from : https://github.com/zongyi-li/fourier_neural_operator (the original author of the fourier neural operator paper) and also from https://github.com/alasdairtran/fourierflow
We created some minor modification on the package to create a proper pip package using fourier neural operator.
You can install it using (after having download the repo)
python setup.py install
or simply using pypi :
pip install fourier-neural-operator
Then to create a fourier model with the pytorch framework, you can write :
import fourier_neural_operator.fourier_2d as fourier_2d
model = fourier_2d.FNO2d(modes1=modes1, modes2=modes2, width=width, channel_input=3, output_channel=3)
You can also simply import fourier layer :
from fourier_neural_operator.fourier_2d.layers.fourier_2d import SpectralConv2d
spectral_layer = SpectralConv2d(width, width, modes1, modes2)
The package is still under construction and modification will come for fourier_3d and 1d.
This repository contains the code for the paper:
In this work, we formulate a new neural operator by parameterizing the integral kernel directly in Fourier space, allowing for an expressive and efficient architecture. We perform experiments on Burgers' equation, Darcy flow, and the Navier-Stokes equation (including the turbulent regime). Our Fourier neural operator shows state-of-the-art performance compared to existing neural network methodologies and it is up to three orders of magnitude faster compared to traditional PDE solvers.
It follows from the previous works:
- (GKN) Neural Operator: Graph Kernel Network for Partial Differential Equations
- (MGKN) Multipole Graph Neural Operator for Parametric Partial Differential Equations
You can check the code in the exemples_paper/ directory.
- We have updated the files to support PyTorch 1.8.0. Pytorch 1.8.0 starts to support complex numbers and it has a new implementation of FFT. As a result the code is about 30% faster.
- Previous version for PyTorch 1.6.0 is avaiable at
FNO-torch.1.6
.
The code is in the form of simple scripts. Each script shall be stand-alone and directly runnable.
exemples_paper/fourier_1d_exemple.py
is the Fourier Neural Operator for 1D problem such as the (time-independent) Burgers equation discussed in Section 5.1 in the paper.exemples_paper/fourier_2d_exemple.py
is the Fourier Neural Operator for 2D problem such as the Darcy Flow discussed in Section 5.2 in the paper.exemples_paper/fourier_2d_time_exemple.py
is the Fourier Neural Operator for 2D problem such as the Navier-Stokes equation discussed in Section 5.3 in the paper, which uses a recurrent structure to propagates in time.exemples_paper/fourier_3d_exemple.py
is the Fourier Neural Operator for 3D problem such as the Navier-Stokes equation discussed in Section 5.3 in the paper, which takes the 2D spatial + 1D temporal equation directly as a 3D problem- The lowrank methods are similar. These scripts are the Lowrank neural operators for the corresponding settings.
data_generation
are the conventional solvers we used to generate the datasets for the Burgers equation, Darcy flow, and Navier-Stokes equation.
We provide the Burgers equation, Darcy flow, and Navier-Stokes equation datasets we used in the paper. The data generation configuration can be found in the paper.
The datasets are given in the form of matlab file. They can be loaded with the scripts provided in utilities.py. Each data file is loaded as a tensor. The first index is the samples; the rest of indices are the discretization. For example,
Burgers_R10.mat
contains the dataset for the Burgers equation. It is of the shape [1000, 8192], meaning it has 1000 training samples on a grid of 8192.NavierStokes_V1e-3_N5000_T50.mat
contains the dataset for the 2D Navier-Stokes equation. It is of the shape [5000, 64, 64, 50], meaning it has 5000 training samples on a grid of (64, 64) with 50 time steps.
We also provide the data generation scripts at data_generation
.
Here are the pre-trained models. It can be evaluated using eval.py or super_resolution.py.
@misc{li2020fourier,
title={Fourier Neural Operator for Parametric Partial Differential Equations},
author={Zongyi Li and Nikola Kovachki and Kamyar Azizzadenesheli and Burigede Liu and Kaushik Bhattacharya and Andrew Stuart and Anima Anandkumar},
year={2020},
eprint={2010.08895},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
@misc{li2020neural,
title={Neural Operator: Graph Kernel Network for Partial Differential Equations},
author={Zongyi Li and Nikola Kovachki and Kamyar Azizzadenesheli and Burigede Liu and Kaushik Bhattacharya and Andrew Stuart and Anima Anandkumar},
year={2020},
eprint={2003.03485},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
We are currently adding some new work to the repo :
- Factorized Fourier Neural Operator
- Conditioned Fourier Neural Operator