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Python scripts for physics-informed neural networks for corrosion-fatigue prognosis

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Python Scripts for Physics-Informed Neural Networks for Corrosion-Fatigue Prognosis

Welcome to the PML repository for physics-informed neural networks used in corrosion-fatigue prognosis. We will use this repository to disseminate our research in this exciting topic.

In order to run the codes, you will need to install the PINN python package: https://github.com/PML-UCF/pinn.

Citing this repository

Please, cite this repository using:

@misc{2019_dourado_viana_python_corrosion_fatigue,
    author    = {A. Dourado and F. A. C. Viana},
    title     = {Python Scripts for Physics-Informed Neural Networks for Corrosion-Fatigue Prognosis},
    month     = Aug,
    year      = 2019,
    doi       = {10.5281/zenodo.3355729},
    version   = {0.0.1},
    publisher = {Zenodo},
    url       = {https://github.com/PML-UCF/pinn_corrosion_fatigue}
    }

The corresponding reference entry should look like:

A. Dourado and F. A. C. Viana, Python Scripts for Physics-Informed Neural Networks for Corrosion-Fatigue Prognosis, v0.0.1, Zenodo, https://github.com/PML-UCF/pinn_corrosion_fatigue, doi:10.5281/zenodo.3355729.

Publications

Over time, the following publications out of the PML-UCF research group used/referred to this repository:

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