PyTorch code for Composing Partial Differential Equations with Physics-Aware Neural Networks

Last update: Dec 18, 2022

Related tags

Overview

FInite volume Neural Network (FINN)

This repository contains the PyTorch code for models, training, and testing, and Python code for data generation to conduct the experiments as reported in the work Composing Partial Differential Equations with Physics-Aware Neural Networks

If you find this repository helpful, please cite our work:

@article{karlbauer2021composing,
	author    = {Karlbauer, Matthias and Praditia, Timothy and Otte, Sebastian and Oladyshkin, Sergey and Nowak, Wolfgang and Butz, Martin V},
	title     = {Composing Partial Differential Equations with Physics-Aware Neural Networks},
	journal   = {arXiv preprint arXiv:2111.11798},
	year      = {2021},
}

Dependencies

We recommend setting up an (e.g. conda) environment with python 3.7 (i.e. conda create -n finn python=3.7). The required packages for data generation and model evaluation are

conda install -c anaconda numpy scipy
conda install -c pytorch pytorch==1.9.0
conda install -c jmcmurray json
conda install -c conda-forge matplotlib torchdiffeq jsmin

Models & Experiments

The code of the different pure machine learning models (TCN, ConvLSTM, DISTANA) and physics-aware models (PINN, PhyDNet, FINN) can be found in the models directory.

Each model directory contains a config.json file to specify model parameters, data, etc. Please modify the sections in the respective config.json files as detailed below (further information about data and model architectures is reported in the according data sections of the paper's appendices):

"training": {
	"t_stop": 150  // burger and allen-cahn 150, diff-sorp 400, diff-react 70
},

"validation": {
	"t_start": 150,  // burger and allen-cahn 150, diff-sorp 400, diff-react 70
	"t_stop": 200  // burger and allen-cahn 200, diff-sorp 500, diff-react 100
},

"data": {
	"type": "burger",  // "burger", "diffusion_sorption", "diffusion_reaction", "allen_cahn"
	"name": "data_ext",  // "data_train", "data_ext", "data_test"
}

"model": {
  	"name": "burger"  // "burger", "diff-sorp", "diff-react", "allen-cahn"
	"field_size": [49],  // burger and allen-cahn [49], diff-sorp [26], fhn [49, 49]
	... other settings to be specified according to the model architectures section in the paper's appendix
}

The actual models can be trained and tested by calling the according python train.py or python test.py scripts. Alternatively, python experiment.py can be used to either train or test n models (please consider the settings in the experiment.py script).

Data generation

The Python scripts to generate the burger, diffusion-sorption, diffusion-reaction, and allen-cahn data can be found in the data directory.

In each of the burger, diffusion_sorption, diffusion_reaction, and allen-cahn directories, a data_generation.py and simulator.py script can be found. The former is used to generate train, extrapolation (ext), or test data. For details about the according data generation settings of each dataset, please refer to the corresponding data sections in the paper's appendices.

You might also like...

Official implementation for the paper: "Multi-label Classification with Partial Annotations using Class-aware Selective Loss"

Multi-label Classification with Partial Annotations using Class-aware Selective Loss Paper | Pretrained models Official PyTorch Implementation Emanuel

99 Dec 27, 2022

Must-read Papers on Physics-Informed Neural Networks.

PINNpapers Contributed by IDRL lab. Introduction Physics-Informed Neural Network (PINN) has achieved great success in scientific computing since 2017.

330 Jan 7, 2023

Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs

PhyCRNet Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs Paper link: [ArXiv] By: Pu Ren, Chengping Rao, Yang

11 Aug 23, 2022

Physics-Aware Training (PAT) is a method to train real physical systems with backpropagation.

Physics-Aware Training (PAT) is a method to train real physical systems with backpropagation. It was introduced in Wright, Logan G. & Onodera, Tatsuhiro et al. (2021)1 to train Physical Neural Networks (PNNs) - neural networks whose building blocks are physical systems.

230 Jan 5, 2023

Pytorch Implementation of Interaction Networks for Learning about Objects, Relations and Physics

Interaction-Network-Pytorch Pytorch Implementraion of Interaction Networks for Learning about Objects, Relations and Physics. Interaction Network is a

117 Nov 5, 2022

IDRLnet, a Python toolbox for modeling and solving problems through Physics-Informed Neural Network (PINN) systematically.

IDRLnet IDRLnet is a machine learning library on top of PyTorch. Use IDRLnet if you need a machine learning library that solves both forward and inver

105 Dec 17, 2022

Releases(v1.0.0)

v1.0.0(Oct 28, 2022)

This release contains the PyTorch code for models, training, and testing, and Python code for data generation to conduct the experiments.
Source code(tar.gz)
Source code(zip)

PyTorch code for Composing Partial Differential Equations with Physics-Aware Neural Networks

Related tags

Overview

FInite volume Neural Network (FINN)

Dependencies

Models & Experiments

Data generation

You might also like...

Official implementation for the paper: "Multi-label Classification with Partial Annotations using Class-aware Selective Loss"

Must-read Papers on Physics-Informed Neural Networks.

Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs

Physics-Aware Training (PAT) is a method to train real physical systems with backpropagation.

Pytorch Implementation of Interaction Networks for Learning about Objects, Relations and Physics

IDRLnet, a Python toolbox for modeling and solving problems through Physics-Informed Neural Network (PINN) systematically.

PINN(s): Physics-Informed Neural Network(s) for von Karman vortex street

Implementation of "Scaled-YOLOv4: Scaling Cross Stage Partial Network" using PyTorch framwork.

Unofficial pytorch implementation of 'Image Inpainting for Irregular Holes Using Partial Convolutions'

Releases(v1.0.0)

v1.0.0(Oct 28, 2022)

Owner

Cognitive Modeling

Pre-trained model, code, and materials from the paper "Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation" (MICCAI 2019).

Updated for TTS(CE) = Also Known as TTN V3. The code requires the first server to be 'ttn' protocol.

PyTorch implementation of residual gated graph ConvNets, ICLR’18

基于Paddle框架的fcanet复现

To model the probability of a soccer coach leave his/her team during Campeonato Brasileiro for 10 chosen teams and considering years 2018, 2019 and 2020.

HistoKT: Cross Knowledge Transfer in Computational Pathology

Kaggle-titanic - A tutorial for Kaggle's Titanic: Machine Learning from Disaster competition. Demonstrates basic data munging, analysis, and visualization techniques. Shows examples of supervised machine learning techniques.

Unrolled Generative Adversarial Networks

Direct design of biquad filter cascades with deep learning by sampling random polynomials.

PyTorch implementation of NeurIPS 2021 paper: "CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration"

Implementation of H-Transformer-1D, Hierarchical Attention for Sequence Learning using 🤗 transformers

ICLR21 Tent: Fully Test-Time Adaptation by Entropy Minimization

Supplemental Code for "ImpressionNet :A Multi view Approach to Predict Socio Facial Impressions"

RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation

Recall Loss for Semantic Segmentation (This repo implements the paper: Recall Loss for Semantic Segmentation)

Count the MACs / FLOPs of your PyTorch model.

Example Of Fine-Tuning BERT For Named-Entity Recognition Task And Preparing For Cloud Deployment Using Flask, React, And Docker

PyTorch implementation of "ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context" (INTERSPEECH 2020)

YOLTv5 rapidly detects objects in arbitrarily large aerial or satellite images that far exceed the ~600×600 pixel size typically ingested by deep learning object detection frameworks

A higher performance pytorch implementation of DeepLab V3 Plus(DeepLab v3+)