AW-PINN for solving 3D flow over a single tall building

Adoptive weights Physics-Informed Neural networks for solving 3D turbulent flow in steady-state condition. due to complexity of case only L-BFGS optimization method is considered. A kind of novel adaptive method for weight balance is applied for making the trainer more robust. L2 regularization in Adam optimization is considered.

Caution:

the main file is adoptive_LBGFS_singleCube.py . The data file is created in csv format but there is no restriction for any kind of data for input. vtk or mat can be easily considered.

** this code is naïve and needs evolution **

This case solves 3D flow over a city.the benchmark is Case E in Aij institute:

_Guidebook for CFD Predictions of Urban Wind Environment Architectural Institute of Japan.

Step 1 - How to run this example

• provide data and boundary condition points file in csv format.
• share your data in your gdrive
• load your drive in colbab
• open the repository github link with google colab
• Run All

Step 2 - consideration

• Epochs number is up to your case
• it's recommended to run it with GPU

Step 3 - Libraries

• torch
• pandas
• numpy
• matplotlib
• sklearn _optional

you can install these libraries in one line easily. just copy this single in in your console:

• pip

  python -m pip install torch pandas numpy matplotlib sklearn

Contact

Amirreza Rezayan - [email protected]