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.
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.
Over time, the following publications out of the PML-UCF research group used/referred to this repository:
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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).
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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, Online first, 2020. (DOI: 10.1115/1.4047173).
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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).
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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.
