henkes_pinn

Code of the publication "Physics informed neural networks for continuum micromechanics" published in https://doi.org/10.1016/j.cma.2022.114790 by Alexander Henkes and Henning Wessels from TU Braunschweig and Rolf Mahnken from University of Paderborn.

https://github.com/ahenkes1/henkes_pinn

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
    Found 5 DOI reference(s) in README
  • Academic publication links
    Links to: zenodo.org
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (12.1%) to scientific vocabulary
Last synced: 9 months ago · JSON representation ·

Repository

Code of the publication "Physics informed neural networks for continuum micromechanics" published in https://doi.org/10.1016/j.cma.2022.114790 by Alexander Henkes and Henning Wessels from TU Braunschweig and Rolf Mahnken from University of Paderborn.

Basic Info
  • Host: GitHub
  • Owner: ahenkes1
  • License: gpl-3.0
  • Language: Python
  • Default Branch: main
  • Size: 436 KB
Statistics
  • Stars: 17
  • Watchers: 1
  • Forks: 3
  • Open Issues: 0
  • Releases: 1
Created almost 4 years ago · Last pushed almost 4 years ago
Metadata Files
Readme License Citation

README.md

HENKES_PINN

DOI

Code of the publication "Physics informed neural networks for continuum micromechanics" published in https://doi.org/10.1016/j.cma.2022.114790 by Alexander Henkes and Henning Wessels from TU Braunschweig and Rolf Mahnken from University of Paderborn.

Please cite the following paper:

@article{henkes2022physics,
  title={Physics informed neural networks for continuum micromechanics},
  author={Henkes, Alexander and Wessels, Henning and Mahnken, Rolf},
  journal={Computer Methods in Applied Mechanics and Engineering},
  volume={393},
  pages={114790},
  year={2022},
  publisher={Elsevier}
}

... and the code using the CITATION.cff file.

Requirements

The requirements can be found in

requirements.txt

and may be installed via pip:

pip install -r requirements.txt

Docker image

You can download a pre-built Docker image via:

docker pull ahenkes1/pinn:1.0.0

If you want to build the Docker image, the official TensorFlow image is needed:

https://www.tensorflow.org/install/docker

Build via

docker build -f ./Dockerfile --pull -t ahenkes1/pinn:1.0.0 .

Execute via

docker run --gpus all -it -v YOUR_LOCAL_OUTPUT_FOLDER:/home/docker_user/src/saved_nets/CPINN/ --rm ahenkes1/pinn:1.0.0 --help

where 'YOURLOCALOUTPUT_FOLDER' is an absolute path to a directory on your system. This will show the help.

Execute the code using standard parameters as

docker run --gpus all -it -v YOUR_LOCAL_OUTPUT_FOLDER:/home/docker_user/src/saved_nets/CPINN/ --rm ahenkes1/pinn:1.0.0 

Using XLA

The code may run using XLA (faster) using the following flag:

XLA_FLAGS=--xla_gpu_cuda_data_dir=/usr/local/cuda-11.2 python3 main.py --help

where the correct cuda path and version have to be used. The Docker image runs XLA natively.

GPU

The code runs on GPU natively using single precision. It was observed, that on some GPUs, which are not capable of double precision calculations, the BFGS algorithm used may interupt before the desired number of iterations is reached. In this case, either switch your GPU, use CPU computation or try mixed precision loss scaling described in https://www.tensorflow.org/guide/mixedprecision#lossscaling. There is no plan to tackle this problem in the next future.

Owner

  • Name: Alexander Henkes
  • Login: ahenkes1
  • Kind: user

Citation (CITATION.cff)

cff-version: "1.2.0"
title: "Physics informed neural networks for continuum micromechanics"
message: "If you use this software, please cite both the article from
preferred-citation and the software itself."
type: "software"

authors: 
    - given-names: "Alexander"
      family-names: "Henkes"
      email: "a.henkes@tu-braunschweig.de"
      affiliation: "TU Braunschweig"
      orcid: "https://orcid.org/0000-0003-4615-9271"

    - given-names: "Henning"
      family-names: "Wessels"
      email: "h.wessels@tu-braunschweig.de"
      affiliation: "TU Braunschweig"
      orcid: "https://orcid.org/0000-0002-2542-1130"

    - given-names: "Rolf"
      family-names: "Mahnken"
      email: "mahnken@ltm.upb.de"
      affiliation: "University of Paderborn"

version: "1.0"
doi: "10.5281/zenodo.6913329"
date-released: "2022-07-26"
url: "https://github.com/ahenkes1/HENKES_PINN"

preferred-citation:
    authors:
        - given-names: 'Alexander '
          family-names: Henkes
          email: a.henkes@tu-braunschweig.de
          affiliation: TU Braunschweig
          orcid: ' https://orcid.org/0000-0003-4615-9271'

        - given-names: Henning
          family-names: Wessels
          email: h.wessels@tu-braunschweig.de
          affiliation: TU Braunschweig
          orcid: ' https://orcid.org/0000-0002-2542-1130 '

        - given-names: Rolf
          family-names: Mahnken
          email: mahnken@ltm.upb.de
          affiliation: University of Paderborn

    title: "Physics informed neural networks for continuum micromechanics"
    journal: "Computer Methods in Applied Mechanics and Engineering"
    publisher:
        name: "Elsevier"
    volume: "393"
    pages: "114790"
    year: "2022"
    type: "article"

references:
    - type: "article"
      authors: 
        - name: "Hunter, J. D."
      title: "Matplotlib: A 2D graphics environment"
      journal: "Computing in Science and Engineering"
      volume: "9"
      number: "3"
      pages: "90--95"
      publisher: 
        name: "IEEE COMPUTER SOC"
      doi: "10.1109/MCSE.2007.55"
      year: "2007"
      
    - type: "article"
      title: "Array programming with NumPy"
      authors:
        - name: "Charles R. Harris and K. Jarrod Millman and Stefan J.
                 van der Walt and Ralf Gommers and Pauli Virtanen and David
                 Cournapeau and Eric Wieser and Julian Taylor and Sebastian
                 Berg and Nathaniel J. Smith and Robert Kern and Matti Picus
                 and Stephan Hoyer and Marten H. van Kerkwijk and Matthew
                 Brett and Allan Haldane and Jaime Fernandez del
                 Rio and Mark Wiebe and Pearu Peterson and Pierre
                 Gerard-Marchant and Kevin Sheppard and Tyler Reddy and
                 Warren Weckesser and Hameer Abbasi and Christoph Gohlke and
                 Travis E. Oliphant"
      year: "2020"
      month: "9"
      journal: "Nature"
      volume: "585"
      number: "7825"
      pages: "357--362"
      doi: "10.1038/s41586-020-2649-2"
      publisher:
        name: "Springer Science and Business Media LLC"
      url: "https://doi.org/10.1038/s41586-020-2649-2"

    - type: "article"
      authors:
        - name: "Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo,
          Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean,
          Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey
          Irving, Michael Isard, Rafal Jozefowicz, Yangqing Jia, Lukasz Kaiser,
          Manjunath Kudlur, Josh Levenberg, Dan Mané, Mike Schuster, Rajat
          Monga, Sherry Moore, Derek Murray, Chris Olah, Jonathon Shlens, Benoit
          Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke,
          Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin
          Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng."
      title: "TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems"
      url: "https://www.tensorflow.org/"

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Dependencies

requirements.txt pypi
  • Keras-Preprocessing ==1.1.2
  • Markdown ==3.4.1
  • Pillow ==9.2.0
  • Werkzeug ==2.1.2
  • absl-py ==1.1.0
  • astunparse ==1.6.3
  • cachetools ==5.2.0
  • certifi ==2022.6.15
  • charset-normalizer ==2.1.0
  • cloudpickle ==2.1.0
  • cycler ==0.11.0
  • decorator ==5.1.1
  • dm-tree ==0.1.7
  • flatbuffers ==1.12
  • fonttools ==4.34.4
  • gast ==0.4.0
  • google-auth ==2.9.1
  • google-auth-oauthlib ==0.4.6
  • google-pasta ==0.2.0
  • grpcio ==1.47.0
  • h5py ==3.7.0
  • idna ==3.3
  • importlib-metadata ==4.12.0
  • keras ==2.9.0
  • kiwisolver ==1.4.4
  • libclang ==14.0.1
  • matplotlib ==3.5.2
  • numpy ==1.23.1
  • oauthlib ==3.2.0
  • opt-einsum ==3.3.0
  • packaging ==21.3
  • protobuf ==3.19.4
  • pyasn1 ==0.4.8
  • pyasn1-modules ==0.2.8
  • pyparsing ==3.0.9
  • python-dateutil ==2.8.2
  • requests ==2.28.1
  • requests-oauthlib ==1.3.1
  • rsa ==4.8
  • six ==1.16.0
  • tensorboard ==2.9.1
  • tensorboard-data-server ==0.6.1
  • tensorboard-plugin-wit ==1.8.1
  • tensorflow ==2.9.1
  • tensorflow-estimator ==2.9.0
  • tensorflow-io-gcs-filesystem ==0.26.0
  • tensorflow-probability ==0.17.0
  • termcolor ==1.1.0
  • typing_extensions ==4.3.0
  • urllib3 ==1.26.10
  • wrapt ==1.14.1
  • zipp ==3.8.1