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I thank reviewer 3 for raising this issue (rephrased by AHL):
The normalised curvature matrix [used to find the optimal value of alpha] is constructed numerically from chi2 using a forward Euler me…
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Thanks you for sharing this library.
In some case using forward automatic differentiation can be useful, for example when solving non-linear least square problems, where having the jacobian matrix o…
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**Description of bug**
Weird behavior of automatic differentiation: error is thrown for a function, but not for a slightly rewritten (though analogous) function or for a bit more complicated one.
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## Implementing Automatic Differentiator
This issue tracks the implementation of an automatic differentiator for the expression compiler. The compiler parses expressions into binary trees, and this…
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The codebase currently uses an inelegant but reasonable finite difference approximation to calculate the Jacobian of a geometry's parametric function.
https://github.com/mikeingold/MeshIntegrals.jl…
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After talking with @bartgol at the EESM meeting last week, he suggested making a tracking issue to discuss changes that would be useful or necessary for differentiable modeling, specifically with auto…
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Perhaps chatGPT can help a lot.
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It might be nice to add support for [ImplicitDifferentiation.jl](https://github.com/JuliaDecisionFocusedLearning/ImplicitDifferentiation.jl) or similar to make the reconstructed image differentiable w…
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Hi!
I had been playing around with using `TaylorDiff.jl` for higher-order differentiation of numerical solutions of ODEs. I have a simple implementation of the method introduced in Appendix D in […
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Should this be implemented using an AD object? Can this AD object handle linear algebra? How much of it is orthogonal to numeric?