Physics-informed neural networks package

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

Install

To install the stable version just do:

pip install pml-pinn

Develop mode

To install in develop mode, clone this repository and do a pip install:

git clone https://github.com/PML-UCF/pinn.git
cd pinn
pip install -e .

Citing this repository

Please, cite this repository using:

@misc{2019_pinn,
    author    = {Felipe A. C. Viana and Renato G. Nascimento and Yigit Yucesan and Arinan Dourado},
    title     = {Physics-informed neural networks package},
    month     = Aug,
    year      = 2019,
    doi       = {10.5281/zenodo.3356876},
    version   = {0.0.3},
    publisher = {Zenodo},
    url       = {https://github.com/PML-UCF/pinn}
    }

The corresponding reference entry should look like:

F. A. C. Viana, R. G. Nascimento, Y. Yucesan, and A. Dourado, Physics-informed neural networks package, v0.0.3, Aug. 2019. doi:10.5281/zenodo.3356876, URL https://github.com/PML-UCF/pinn.

Publications

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

Journal papers

• R. G. Nascimento and F. A. C. Viana, "Cumulative damage modeling with recurrent neural networks," AIAA Journal, Online First, 13 pages, 2020. (DOI: 10.2514/1.J059250).

• A. Dourado and F. A. C. Viana, "Physics-informed neural networks for missing physics estimation in cumulative damage models: a case study in corrosion fatigue," ASME Journal of Computing and Information Science in Engineering, Vol. 20 (6), 10 pages, 2020. (DOI: 10.1115/1.4047173).

• Y. A. Yucesan and F. A. C. Viana, "A physics-informed neural network for wind turbine main bearing fatigue," International Journal of Prognostics and Health Management, Vol. 11 (1), 2020. (ISSN: 2153-2648).

Conference papers

• A. Dourado and F. A. C. Viana, "Physics-informed neural networks for bias compensation in corrosion-fatigue," AIAA SciTech Forum, Orlando, USA, January 6-10, 2020, AIAA 2020-1149 (DOI: 10.2514/6.2020-1149).

• Y. A. Yucesan and F. A. C. Viana, "A hybrid model for main bearing fatigue prognosis based on physics and machine learning," AIAA SciTech Forum, Orlando, USA, January 6-10, 2020, AIAA 2020-1412 (DOI: 10.2514/6.2020-1412).

• A. Dourado and F. A. C. Viana, "Physics-Informed Neural Networks for Corrosion-Fatigue Prognosis," Proceedings of the Annual Conference of the PHM Society, Scottsdale,USA, September 21-26, 2019.

• Y. A. Yucesan and F. A. C. Viana, "Wind turbine main bearing fatigue life estimation with physics-informed neural networks," Proceedings of the Annual Conference of the PHM Society, Vol. 11 (1), Scottsdale, USA, September 21-26, 2019 (DOI:10.36001/phmconf.2019.v11i1.807).

• R.G. Nascimento and F. A. C. Viana, "Fleet prognosis with physics-informed recurrent neural networks," The 12th International Workshop on Structural Health Monitoring, Stanford, USA, September 10-12, 2019 (DOI:10.12783/shm2019/32301).