PINN VFM

There are some datasets available. We build dataset01 (using main_build_dataset.py) for training purposes and dataset_opera (using main_build_dataset.py) to verify the model generability.

The procedure to achieve a good model is:
a) start the training using model_adam_200 (main_train_adam.py)
b) complete the training changing to main_train_lbfgs resulting in model_adam_lbfgs model
c) pay attention to the loss terms evolution to set a better set of weights

The notebook main.ipynb demonstrates the model capability during free prediction

Feel free to contribute with the software improvements. If this code help you, you are encouraged to cite the following paper:

Citation

@article{FRANKLIN2022,
title = {A Physics-Informed Neural Networks (PINN) oriented approach to flow metering in oil wells: an ESP lifted oil well system as a case study},
journal = {Digital Chemical Engineering},
pages = {100056},
year = {2022},
issn = {2772-5081},
doi = {https://doi.org/10.1016/j.dche.2022.100056},
url = {https://www.sciencedirect.com/science/article/pii/S2772508122000461},
author = {Taniel S. Franklin and Leonardo S. Souza and Raony M. Fontes and Márcio A.F. Martins},
keywords = {soft sensor, Physics-Informed Neural Networks, electrical submersible pump, virtual flow meter, recurrent neural network},