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

• A. Dourado, F. A. C. Viana, "Ensemble of hybrid neural networks to compensate for epistemic uncertainties: a case study in system prognosis," Soft Computing, Vol. 26 (13), pp. 6157-6173, 2022. (DOI: 10.1007/s00500-022-07129-1).

• Y. A. Yucesan and F. A. C. Viana, "A hybrid physics-informed neural network for main bearing fatigue prognosis under grease quality variation," Mechanical Systems and Signal Processing, Vol. 171, pp. 108875, 2022. (DOI: 10.1016/j.ymssp.2022.108875).

• Y. A. Yucesan, A. Dourado, and F. A. C. Viana, "A survey of modeling for prognosis and health management of industrial equipment," Advanced Engineering Informatics, Vol. 50, pp. 101404, 2021. (DOI: 10.1016/j.aei.2021.101404).

• F. A. C. Viana and A. K. Subramaniyan, "A survey of Bayesian calibration and physics-informed neural networks in scientific modeling," Archives of Computational Methods in Engineering, Vol. 28 (5), pp. 3801-3830, 2021. (DOI: 10.1007/s11831-021-09539-0). .

• Y. A. Yucesan and F. A. C. Viana, "Hybrid physics-informed neural networks for main bearing fatigue prognosis with visual grease inspection," Computers in Industry, Computers in Industry, Vol. 125, pp. 103386, 2021. (DOI: 10.1016/j.compind.2020.103386).

• F. A. C. Viana, R. G. Nascimento, A. Dourado, and Y. A. Yucesan, "Estimating model inadequacy in ordinary differential equations with physics-informed neural networks," Computers and Structures, Vol. 245, pp. 106458, 2021. (DOI: 10.1016/j.compstruc.2020.106458).

• 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

• Y. A. Yucesan and F. A. C. Viana, "A probabilistic hybrid model for main bearing fatigue considering uncertainty in grease quality," AIAA Scitech 2021 Forum, Virtual Event, January 11-15 and 19-21, 2021, AIAA–2021–1243 (DOI: 10.2514/6.2021-1243).

• Y. A. Yucesan and F. A. C. Viana, "A hybrid model for wind turbine main bearing fatigue with uncertainty in grease observations," Proceedings of the Annual Conference of the PHM Society, Vol. 12 (1), Virtual Event, November 9-13, 2020 (DOI: 10.36001/phmconf.2020.v12i1.1139).

• 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 bias compensation in corrosion-fatigue," AIAA SciTech Forum, Orlando, USA, January 6-10, 2020, AIAA 2020-1149 (DOI: 10.2514/6.2020-1149).

• 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).