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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. arXiv preprint arXiv:2102.04626, 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

Cite this work

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

@article{lu2021physics,
  title   = {Physics-informed neural networks with hard constraints for inverse design},
  author  = {Lu, Lu and Pestourie, Raphael and Yao, Wenjie and Wang, Zhicheng and Verdugo, Francesc and Johnson, Steven G},
  journal = {arXiv preprint arXiv:2102.04626},
  year    = {2021}
}

Questions

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

hpinn's People

Contributors

lululxvi avatar rpestourie avatar

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