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Physics-informed neural networks package
| Date | Stars |
|---|---|
| 2026-07-31 | 347 |
| 2026-08-05 | 347 |
| 2026-08-06 | 347 |
Today
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Momentum
0.0
growth rate 0.00%/day
[](https://doi.org/10.5281/zenodo.3356876)
[](https://badge.fury.io/py/pml-pinn)
# 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](https://link.springer.com/article/10.1007/s00500-022-07129-1)," 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](https://www.sciencedirect.com/science/article/pii/S088832702200070X)," 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](https://www.sciencedirect.com/science/article/pii/S1474034621001567)," 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](https://link.springer.com/article/10.1007/s11831-021-09539-0)," 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](https://www.sciencedirect.com/science/article/pii/S0166361520306205)," 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](https://www.sciencedirect.com/science/article/pii/S0045794920302613)," 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](https://arc.aiaa.org/doi/full/10.2514/1.J059250)," 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](https://asmedigitalcollection.asme.org/computingengineering/article-abstract/doi/10.1115/1.4047173/1083614/Physics-informed-neural-networks-for-missing)," ASME Journal of Computing and Information Science iExcerpt of 6,427 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:3025c645eac59508, llm:description: "Physics-informed neural networks package"
matched fp:3025c645eac59508, llm:description: "Physics-informed neural networks package"
matched fp:3025c645eac59508, llm:description: "Physics-informed neural networks package"