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

Related tags

Deep Learningfinn
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:
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

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.

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

 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.

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.

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

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

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

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

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

YOLOv4-large This is the implementation of "Scaled-YOLOv4: Scaling Cross Stage Partial Network" using PyTorch framwork. YOLOv4-CSP YOLOv4-tiny YOLOv4-

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

pytorch-inpainting-with-partial-conv Official implementation is released by the authors. Note that this is an ongoing re-implementation and I cannot f

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)
Owner
Cognitive Modeling
The chair of Cognitive Modeling addresses the question: "How does the mind work?", pursuing an integrative, interdisciplinary, computational approach.
Cognitive Modeling
VGG16 model-based classification project about brain tumor detection.

Brain-Tumor-Classification-with-MRI VGG16 model-based classification project about brain tumor detection. First, you can check what people are doing o

Atakan Erdoğan 2 Mar 21, 2022
这是一个yolox-keras的源码,可以用于训练自己的模型。

YOLOX:You Only Look Once目标检测模型在Keras当中的实现 目录 性能情况 Performance 实现的内容 Achievement 所需环境 Environment 小技巧的设置 TricksSet 文件下载 Download 训练步骤 How2train 预测步骤 Ho

Bubbliiiing 64 Nov 10, 2022
NeoPlay is the project dedicated to ESport events.

NeoPlay is the project dedicated to ESport events. On this platform users can participate in tournaments with prize pools as well as create their own tournaments.

3 Dec 18, 2021
Using Hotel Data to predict High Value And Potential VIP Guests

Description Using hotel data and AI to predict high value guests and potential VIP guests. Hotel can leverage on prediction resutls to run more effect

HCG 12 Feb 14, 2022
Discretized Integrated Gradients for Explaining Language Models (EMNLP 2021)

Discretized Integrated Gradients for Explaining Language Models (EMNLP 2021) Overview of paths used in DIG and IG. w is the word being attributed. The

INK Lab @ USC 17 Oct 27, 2022
A graph adversarial learning toolbox based on PyTorch and DGL.

GraphWar: Arms Race in Graph Adversarial Learning NOTE: GraphWar is still in the early stages and the API will likely continue to change. 🚀 Installat

Jintang Li 54 Jan 05, 2023
Official source code of Fast Point Transformer, CVPR 2022

Fast Point Transformer Project Page | Paper This repository contains the official source code and data for our paper: Fast Point Transformer Chunghyun

182 Dec 23, 2022
A variational Bayesian method for similarity learning in non-rigid image registration (CVPR 2022)

A variational Bayesian method for similarity learning in non-rigid image registration We provide the source code and the trained models used in the re

daniel grzech 14 Nov 21, 2022
Grow Function: Generate 3D Stacked Bifurcating Double Deep Cellular Automata based organisms which differentiate using a Genetic Algorithm...

Grow Function: A 3D Stacked Bifurcating Double Deep Cellular Automata which differentiates using a Genetic Algorithm... TLDR;High Def Trees that you can mint as NFTs on Solana

Nathaniel Gibson 4 Oct 08, 2022
Google-drive-to-sqlite - Create a SQLite database containing metadata from Google Drive

google-drive-to-sqlite Create a SQLite database containing metadata from Google

Simon Willison 140 Dec 04, 2022
Code for Emergent Translation in Multi-Agent Communication

Emergent Translation in Multi-Agent Communication PyTorch implementation of the models described in the paper Emergent Translation in Multi-Agent Comm

Facebook Research 75 Jul 15, 2022
Back to Basics: Efficient Network Compression via IMP

Back to Basics: Efficient Network Compression via IMP Authors: Max Zimmer, Christoph Spiegel, Sebastian Pokutta This repository contains the code to r

IOL Lab @ ZIB 1 Nov 19, 2021
This repository is the official implementation of the Hybrid Self-Attention NEAT algorithm.

This repository is the official implementation of the Hybrid Self-Attention NEAT algorithm. It contains the code to reproduce the results presented in the original paper: https://arxiv.org/abs/2112.0

Saman Khamesian 6 Dec 13, 2022
Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting

QAConv Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting This PyTorch code is proposed in

Shengcai Liao 166 Dec 28, 2022
Greedy Gaussian Segmentation

GGS Greedy Gaussian Segmentation (GGS) is a Python solver for efficiently segmenting multivariate time series data. For implementation details, please

Stanford University Convex Optimization Group 72 Dec 07, 2022
Official PyTorch implementation of paper: Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation (ICCV 2021 Oral Presentation)

SML (ICCV 2021, Oral) : Official Pytorch Implementation This repository provides the official PyTorch implementation of the following paper: Standardi

SangHun 61 Dec 27, 2022
Instant-nerf-pytorch - NeRF trained SUPER FAST in pytorch

instant-nerf-pytorch This is WORK IN PROGRESS, please feel free to contribute vi

94 Nov 22, 2022
This repository contains the code for designing risk bounded motion plans for car-like robot using Carla Simulator.

Nonlinear Risk Bounded Robot Motion Planning This code simulates the bicycle dynamics of car by steering it on the road by avoiding another static car

8 Sep 03, 2022
This repository compare a selfie with images from identity documents and response if the selfie match.

aws-rekognition-facecompare This repository compare a selfie with images from identity documents and response if the selfie match. This code was made

1 Jan 27, 2022
Image inpainting using Gaussian Mixture Models

dmfa_inpainting Source code for: MisConv: Convolutional Neural Networks for Missing Data (to be published at WACV 2022) Estimating conditional density

Marcin Przewięźlikowski 8 Oct 09, 2022