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

TART - A PyTorch implementation for Transition Matrix Representation of Trees with Transposed Convolutions

A Pytorch implementation of CVPR 2021 paper "RSG: A Simple but Effective Module for Learning Imbalanced Datasets"

RDA: Robust Domain Adaptation via Fourier Adversarial Attacking

HCQ: Hybrid Contrastive Quantization for Efficient Cross-View Video Retrieval

Official Implementation of VAT

Deep Learning to Improve Breast Cancer Detection on Screening Mammography

Ready-to-use code and tutorial notebooks to boost your way into few-shot image classification.

This repository holds the code for the paper "Deep Conditional Gaussian Mixture Model forConstrained Clustering".

LeafSnap replicated using deep neural networks to test accuracy compared to traditional computer vision methods.

The repository for our EMNLP 2021 paper "Finnish Dialect Identification: The Effect of Audio and Text"

PyTorch Code for "Generalization in Dexterous Manipulation via Geometry-Aware Multi-Task Learning"

A list of all papers and resoureces on Semantic Segmentation

Fast Soft Color Segmentation

[arXiv'22] Panoptic NeRF: 3D-to-2D Label Transfer for Panoptic Urban Scene Segmentation

Implementation of Basic Machine Learning Algorithms on small datasets using Scikit Learn.

A PyTorch implementation of SIN: Superpixel Interpolation Network

Demonstration of transfer of knowledge and generalization with distillation

Torch code for our CVPR 2018 paper "Residual Dense Network for Image Super-Resolution" (Spotlight)

Greedy Gaussian Segmentation

Tool cek opsi checkpoint facebook!