Physics Informed PointNet (PIPN)

Author: Ali Kashefi (kashefi@stanford.edu)
Description: Implementation of physics-informed PointNet (PIPN) for weakly-supervised learning of incompressible flows and thermal fields on irregular geometries
Version: 1.0

Citation
If you use the code, plesae cite the following journal paper:

Physics-informed PointNet: A deep learning solver for steady-state incompressible flows and thermal fields on multiple sets of irregular geometries

 @article{Kashefi2022PIPN, 
   title = {Physics-informed PointNet: A deep learning solver for steady-state incompressible flows and thermal fields on multiple sets of irregular geometries},
   journal = {Journal of Computational Physics}, 
   volume = {468}, 
   pages = {111510}, 
   year = {2022}, 
   issn = {0021-9991},
   author = {Ali Kashefi and Tapan Mukerji}}

Physics-informed PointNet on Wikipedia
A general description of physics-informed neural networks (PINNs) and its other versions such as PIPN can be found in the following Wikipedia page:
Physics-informed PointNet (PIPN) for multiple sets of irregular geometries

Physics-informed PointNet Presentation in Machine Learning + X seminar 2022 at Brown University
In case of your interest, you might watch the recorded machine learning seminar with the topic of PIPN at Brown University using the following link:
Video Presentation of PIPN at Brown University
YouTube Video

Questions?
If you have any questions or need assistance, please do not hesitate to contact Ali Kashefi (kashefi@stanford.edu) via email.

About the Author
Please see the author's website: Ali Kashefi