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# Autoregressive Renaissance in Neural PDE Solvers | ICLR Blogposts 2023
Recent developments in the field of neural partial differential equation (PDE) solvers have placed a strong emphasis on neural…
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```
def pde_system(x, y, f):
# 1D Convection-Diffusion
c = y[:, 0:1]
#constant velocity for now
u = torch.tensor(0.1)
du_x = 0.
# derivatives
dc_dt = dde.grad…
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# Convolutional neural networks for solving PDEs | Arthur Bauville
Writing an iterative multigrid PDE solver as a convolutional neural network
[https://abauville.github.io/blog/neural%20networks/202…
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I am using Krylov methods to solve a non-symmetric system arising from spectral Galerkin PDE discretization. The system converges with GMRES but it takes quite a lot of iterations. I am not that famil…
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We should definitely organize the `examples` directory into subdirectories as this package becomes larger, but I am not sure if it makes sense to do so based on solver/mesh type the way Trixi.jl does.…
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Here, I'm listing a few cases where the availability of new parameters will be useful for customizing inversions -
- preprocessing
In some cases, the user might need to use their seismograms in th…
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Hi,
I would like to use your “pH Calc” algorithm in my Reactive-Transport-Toolbox.
This reaction toolbox solves advection-diffusion-reaction PDE for saturated (at the moment) and not saturated (…
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Publish the Fourier Neural PDE Solver dataset to Foundry
A list of all potential data targets, and more info on this dataset, can be found here:
https://docs.google.com/spreadsheets/d/1SUrYEBoO1-L…
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This package has potential to be a solid foundation as being the interface between ModelingToolkit.jl and several FEM/FVM/DG/... packages, which can solve PDE-based models on unstructured domains. We …
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#### Summary:
Allow 3rd-party PDE(partial differential equation) libraries to be used to perform inference that involve PDEs.
#### Description:
The design involves `cmdstan`, `stan`, and `math…