# PINN(s): Physics-Informed Neural Network(s) for von Karman vortex street

This is an implementation of PINN(s) on TensorFlow 2 to learn the flow field of von Karman vortex street, and estimate the fluid density and kinemetic viscosity.

## Usage

Simply type
```
python main.py
```

to run the entire code in
```
src
```

directory. This will load data in
```
input
```

and starts training to estimate the fluid density (rho), and kinematic viscosity (nu). Basic parameters (e.g., network architecture, batch size, initializer, etc.) are found in
```
params.py
```

and could be modified depending on the problem setup.

## Environment

Tested on
```
python 3.8.10
```

with the following:

Package | Version |
---|---|

numpy | 1.22.1 |

scipy | 1.7.3 |

tensorflow | 2.8.0 |

## Reference

[1] Raissi, M., Perdikaris, P., Karniadakis, G.E.: Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, *Journal of Computational Physics*, Vol. 378, pp. 686-707, 2019. (paper)