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pinnpapers's Issues

Recommending several papers

Great job! I wish that I had seen this earlier.

I would like to recommend

  1. When and why PINNs fail to train: A neural tangent kernel perspective
  2. On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks
  3. Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets
  4. Long-time integration of parametric evolution equations with physics-informed DeepONets

PINN software addition

Hello👋🏻
Great job in putting a lot of PINN literature just in one place!

I saw you have a software section, and I suggest you look at PINA if you want to add another one! PINA is an open-source Python library providing an intuitive interface for solving differential equations using PINNs, NOs or both together based on PyTorch and PyTorchLightning. In addition to implementing the state-of-the-art methodology, we provide advancement training strategies such as multiple device training, modern model compression techniques, gradient accumulation, and so on thanks to PyTorchLightning.

If you find the software interesting and helpful it would be amazing to enter the list! 😄🚀

Paper Link: https://joss.theoj.org/papers/10.21105/joss.05352
GitHub: https://github.com/mathLab/PINA

PINN application paper

Hi, I publish a paper about the PINN application for Li ion battery temp prediction.
Would you put my paper on your list?

G. Cho, M. Wang, Y. Kim, J. Kwon and W. Su, "A Physics-Informed Machine Learning Approach for Estimating Lithium-Ion Battery Temperature," in IEEE Access, vol. 10, pp. 88117-88126, 2022, doi: 10.1109/ACCESS.2022.3199652.

Thank you

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