Factorized Fourier Neural Operators

This repository contains the code to reproduce the results in our NeurIPS 2021 ML4PS workshop paper, Factorized Fourier Neural Operators.

The Fourier Neural Operator (FNO) is a learning-based method for efficiently simulating partial differential equations. We propose the Factorized Fourier Neural Operator (F-FNO) that allows much better generalization with deeper networks. With a careful combination of the Fourier factorization, weight sharing, the Markov property, and residual connections, F-FNOs achieve a six-fold reduction in error on the most turbulent setting of the Navier-Stokes benchmark dataset. We show that our model maintains an error rate of 2% while still running an order of magnitude faster than a numerical solver, even when the problem setting is extended to include additional contexts such as viscosity and time-varying forces. This enables the same pretrained neural network to model vastly different conditions.

Getting Started

# Set up pyenv and pin python version to 3.9.7
curl https://pyenv.run | bash
# Configure our shell's environment for pyenv
pyenv install 3.9.7
pyenv local 3.9.7

# Set up poetry
curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/install-poetry.py | python -
export PATH="$HOME/.local/bin:$PATH"

# Install all python dependencies
poetry install
source .venv/bin/activate # or: poetry shell
# If we need to use Jupyter notebooks
python -m ipykernel install --user --name fourierflow --display-name "fourierflow"
# Temp fix until allennlp has upgraded transformers dependencies to 4.11
poe update-transformers
# Manually reinstall Pytorch with CUDA 11.1 support
# Monitor poetry's support for pytorch here: https://github.com/python-poetry/poetry/issues/2613
poe install-torch-cuda11

# set default paths
cp example.env .env
# The environment variables in .env will be loaded automatically when running
# fourierflow train, but we can also load them manually in our terminal
export $(cat .env | xargs)

# Alternatively, you can pass the paths to the system using env vars, e.g.
FNO_DATA_ROOT=/My/Data/Location fourierflow

Navier Stokes Experiments

You can download all of our datasets and pretrained model as follows:

# Datasets (209GB)
wget --continue https://object-store.rc.nectar.org.au/v1/AUTH_c0e4d64401cf433fb0260d211c3f23f8/fourierflow/data.tar.gz
tar -zxvf data.tar.gz

# Pretrained models and results (30GB)
wget --continue https://object-store.rc.nectar.org.au/v1/AUTH_c0e4d64401cf433fb0260d211c3f23f8/fourierflow/experiments.tar.gz
tar -zxvf experiments.tar.gz

Alternatively, you can also generate the datasets from scratch:

# Download Navier Stokes datasets
fourierflow download fno

# Generate Navier Stokes on toruses with a different forcing function and
# viscosity for each sample. Takes 14 hours.
fourierflow generate navier-stokes --force random --cycles 2 --mu-min 1e-5 \
    --mu-max 1e-4 --steps 200 --delta 1e-4 \
    data/navier-stokes/random_force_mu.h5

# Generate Navier Stokes on toruses with a different time-varying forcing
# function and a different viscosity for each sample. Takes 21 hours.
fourierflow generate navier-stokes --force random --cycles 2 --mu-min 1e-5 \
    --mu-max 1e-4 --steps 200 --delta 1e-4 --varying-force \
    data/navier-stokes/random_varying_force_mu.h5

# If we decrease delta from 1e-4 to 1e-5, generating the same dataset would now
# take 10 times as long, while the difference between the solutions in step 20
# is only 0.04%.

Training and test commands:

# Reproducing SOA model on Navier Stokes from Li et al (2021).
fourierflow train --trial 0 experiments/navier_stokes_4/zongyi/4_layers/config.yaml

# Train with our best model
fourierflow train --trial 0 experiments/navier_stokes_4/markov/24_layers/config.yaml

# Get inference time on test set
fourierflow predict --trial 0 experiments/navier_stokes_4/markov/24_layers/config.yaml

Visualization commands:

# Create all plots and tables for paper
fourierflow plot layer
fourierflow plot complexity
fourierflow plot table-3

# Create the flow animation for presentation
fourierflow plot flow

# Create plots for the poster
fourierflow plot poster

Experiments with Fourier layers on simulation data.

Related tags

Overview

Factorized Fourier Neural Operators

Getting Started

Navier Stokes Experiments

Owner

Alasdair Tran

PaRT: Parallel Learning for Robust and Transparent AI

CLIP (Contrastive Language–Image Pre-training) trained on Indonesian data

Unified learning approach for egocentric hand gesture recognition and fingertip detection

Codes for our paper "SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic Knowledge" (EMNLP 2020)

This repository is maintained for the scientific paper tittled " Study of keyword extraction techniques for Electric Double Layer Capacitor domain using text similarity indexes: An experimental analysis "

The final project of "Applying AI to EHR Data" of "AI for Healthcare" nanodegree - Udacity.

Codes for the paper Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing

Using CNN to mimic the driver based on training data from Torcs

On Size-Oriented Long-Tailed Graph Classification of Graph Neural Networks

(ICCV 2021 Oral) Re-distributing Biased Pseudo Labels for Semi-supervised Semantic Segmentation: A Baseline Investigation.

This repository contains python code necessary to replicated the experiments performed in our paper "Invariant Ancestry Search"

Bi-level feature alignment for versatile image translation and manipulation (Under submission of TPAMI)

A minimal implementation of Gaussian process regression in PyTorch

MEND: Model Editing Networks using Gradient Decomposition

Rainbow DQN implementation that outperforms the paper's results on 40% of games using 20x less data 🌈

Official implementation of Densely connected normalizing flows

Resources for our AAAI 2022 paper: "LOREN: Logic-Regularized Reasoning for Interpretable Fact Verification".

codebase for "A Theory of the Inductive Bias and Generalization of Kernel Regression and Wide Neural Networks"

Deploy pytorch classification model using Flask and Streamlit

Urban mobility simulations with Python3, RLlib (Deep Reinforcement Learning) and Mesa (Agent-based modeling)