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LudwigBoess / SPHKernels.jl

Julia implementation of some common SPH kernels
MIT License
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Potential Future Advanced Functionality: Automatic Differentiation of Kernels #16

Closed AhmedSalih3d closed 2 years ago

AhmedSalih3d commented 3 years ago

Hello

I will not write too much here, but the basic idea is that it would be nice to give the user the option to specify a kernel and then using ForwardDiff.jl, provide numerically equivivalent derivative, gradient, divergence and curl operators. For more about the idea see here:

https://discourse.julialang.org/t/can-i-expect-forwarddiff-to-give-the-same-performance-in-this-case/67894/4

@LudwigBoess I hope this is fine to have here, I just don't want to lose this idea. I might fork this repos in the future, trying to implement this idea for my favourite kernel you have seen a few times (the one in the link) :)

Kind regards

AhmedSalih3d commented 2 years ago

Idea is cool, but does not fit main scope of this project.

Perhaps if some day we would be using different kernels through a simulation or for different equations, so we would have like 10-20 expressions, this could make sense - for now let us not divert attention.