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Have you considered probabilistic programming for error propagation? Automatic differentiation (together with the delta method from statistics) is pretty nice, too -- and popular courtesy of backpropa…
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![autodiff_roadmap_svg](https://user-images.githubusercontent.com/33411325/170226565-0fc8663c-4e31-4842-85d1-9e09abdff817.svg)
I would like to propose an initial roadmap of the Automatic differe…
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Hi,
I would like to use `Math::Matrix4::rotationX` and companions together with automatic differentiation of the ceres solver.
https://github.com/ceres-solver/ceres-solver
http://ceres-solver…
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Partially covered by #19 with Dual types, but probably need more. Look at tests for ExponentialAction for inspiration.
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It would be beneficial to introduce the ability to specify custom color for each specified column on the Events tab.
The current design automatically assigns light colors to specified columns, whic…
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Hi all,
I have been pointed out to this working group by a college. First some background: I am a researcher in Lattice QCD. I have found that the typical data analysis that we usually done is grea…
ramos updated
3 years ago
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In the documentation section for Head gradient and the chain rule, I think it might be better to explain the context behind head gradient in a bit more detailed way.
Like if we refer to the class-no…
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So to run moving mesh problems with perfect Jacobians in MOOSE I've developed methods like [`Assembly::computeSinglePointMapAD`](https://github.com/idaholab/moose/blob/next/framework/src/base/Assembly…
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Since we're capturing the expression graphs, we're in a good position to support automatic differentiation as well. This would require expressions to track a bit more information (the actual arithmet…
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A lot of the factors' gradients can be analytically computed. For others, automatic differentiation can be done with packages such as Zygote or ForwardDiff.
I'm starting this broad issue to look at…
Affie updated
2 years ago