hPINN: Physics-informed neural networks with hard constraints

The source code for the paper L. Lu, R. Pestourie, W. Yao, Z. Wang, F. Verdugo, & S. G. Johnson. Physics-informed neural networks with hard constraints for inverse design. SIAM Journal on Scientific Computing, 43(6), B1105-B1132, 2021.

Code

The code depends on the deep learning package DeepXDE v0.9.1. If you want to use the latest DeepXDE, you need to modify the code.

Holography

Fluids in Stokes flow

hPINN

Cite this work

If you use this code for academic research, you are encouraged to cite the following paper:

@article{lu2021physics,
  author  = {Lu, Lu and Pestourie, Raphael and Yao, Wenjie and Wang, Zhicheng and Verdugo, Francesc and Johnson, Steven G},
  title   = {Physics-informed neural networks with hard constraints for inverse design},
  journal = {SIAM Journal on Scientific Computing},
  volume  = {43},
  number  = {6},
  pages   = {B1105-B1132},
  year    = {2021},
  doi     = {10.1137/21M1397908}
}

Questions

To get help on how to use the code, simply open an issue in the GitHub "Issues" section.

lululxvi / hpinn