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**Description**
I want to calculate nodal normal shape derivatives to make adjoint bossak scheme to work with slip conditions (in the `SensitivityBuilder`). Currently, nodal normals for nodes with SL…
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Hi,
I'm trying to use Zygote.jl up to 2nd order with some external matrix routines, but met a problem when trying to recursively(?) utilize custom gradients.
Here is an example to reproduce:
``…
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It should work by design, but it would be good to get some tests to make sure that it's also calling the adjoint correctly.
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The current implementation of the adjoints on SDEs requires diagonal noise. We should make sure to work out the more general case and add handling for that.
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Hey there,
I'm looking at using Yota for a project where I need fast reverse mode AD but I'm new to writing adjoints and I'm having a bit of a hard time implementing an adjoint for the matrix expon…
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In estimating `MLE` and `MAP`, the routines from `Optim.jl` are called. However, even for gradient-based methods, Optim.jl only support `ForwardDiff` as an AD backend, and apply finite differences oth…
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Currently broadcasting causes a `TrackedArray` to become an array of `TrackedReal`s. It seems that the broadcasting code in `ReverseDiff` (RD) wasn't updated to the latest Julia version because I see …
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Hello SU2 community,
I'm currently interested in the theoretical background used in SU2_DOT (both in the FD or AD option). Is it available somewhere? The use I'm interested in is the propagation o…
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Already in the abstract we have a phrase like "With a bit of elementary mathematics," which is completely unhelpful. Adjoints from category theory, and much else besides, does show up here, and these …
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On the documentation page [LV-Univ.md]( https://diffeqflux.sciml.ai/dev/examples/LV-Univ/), the introductory text states:
> Here's an example of doing this with both reverse-mode autodifferentiatio…