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1. Nodes cannot directly fit multiplication and division operations; nodes can only use addition and subtraction.
2. When using the model, it is not possible to restrict the activation functions used…
hzy24 updated
4 months ago
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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](https://arxiv.org/abs/2007.14527)
2. [On the e…
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[Physics Informed Neural Networks by NVIDIA](https://docs.nvidia.com/deeplearning/modulus/user_guide/theory/recommended_practices.html#physics-informed-neural-networks) ... more generally, [Physics-In…
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Dear Authors,
I'm interesting in your paper "Physics-informed neural networks for inverse problems in nano-optics and metamaterials", in which you used DeepXDE lib. But I cannot find your code publ…
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I just want to know the mainly difference between static and dynamic in meshtype. Why the static is only used in 1D condition? The relative information in the paper "fPINNs: Fractional Physics-Inform…
TLDSZ updated
2 years ago
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I learned the hard bc of example diffusion_1d_exactBC, and your paper:PHYSICS-INFORMED NEURAL NETWORKS WITH HARD
CONSTRAINTS FOR INVERSE DESIGN∗, but I still can not apply hard bc to to other problem…
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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 Le…
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Approximations allow us to include model components that don't have autodiff-friendly implementations.
- A first test case will be approximating the local coupling term in a neural field model
- A…
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Hello @lululxvi @MinZhu123 @ChenxiWu123
Thank you for your excellent sharing. Recently I read your article entitled Effective data sampling strategies and boundary condition constraints of physics-i…
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Hi @awaelchli
So sorry for the last minute question! I have been closely following physics-informed / equivariant neural networks over the last few months, and I was very excited to see our overlap…