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.
Science Score: 67.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓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
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
Metadata Files
README.md
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.
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
- Repositories: 3
- Profile: https://github.com/ahenkes1
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/"
GitHub Events
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- Watch event: 6
Last Year
- Watch event: 6
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Last synced: 9 months ago
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- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
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- Average time to close issues: N/A
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Dependencies
- 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