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byuflowlab
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ImplicitAD.jl
Automates adjoints. Forward and reverse mode algorithmic differentiation around implicit functions (not propagating AD through), as well as custom rules to allow for mixed-mode AD or calling external (non-AD compatible) functions within an AD chain.
MIT License
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Keep getting error with provide_rule
#18
KapilKhanal
opened
1 month ago
1
Update eigenvalues.jl
#17
BTV25
closed
2 months ago
0
Add user defined matrix multiplication function
#16
jmaack24
closed
2 months ago
0
fix typo "matix" in index.html
#15
rymanderson
opened
1 year ago
0
Remove the unused parametric types present after PR 13.
#14
juddmehr
closed
1 year ago
0
Qualify access to TrackedReal and TrackedArray in nonlinear.jl
#13
dingraha
closed
1 year ago
0
Faster unsteady
#12
andrewning
closed
1 year ago
0
add explicit_unsteady and implicit_unsteady
#11
taylormcd
closed
1 year ago
1
Avoid overwriting cached variables before the reverse pass
#10
taylormcd
closed
1 year ago
0
Partial derivative matrix cannot be safely re-used.
#9
taylormcd
closed
1 year ago
0
Dual Numbers in (Constant) Parameters
#8
taylormcd
opened
1 year ago
0
Alternative method for providing partial derivatives?
#7
taylormcd
closed
1 year ago
1
relax compatibility requirements
#6
taylormcd
closed
1 year ago
1
TagBot trigger issue
#5
JuliaTagBot
closed
1 year ago
4
Differences with ImplicitDifferentiation.jl?
#4
gdalle
closed
1 year ago
12
CompatHelper: add new compat entry for ChainRulesCore at version 1, (keep existing compat)
#3
github-actions[bot]
closed
1 year ago
0
CompatHelper: add new compat entry for ForwardDiff at version 0.10, (keep existing compat)
#2
github-actions[bot]
closed
1 year ago
0
CompatHelper: add new compat entry for ReverseDiff at version 1, (keep existing compat)
#1
github-actions[bot]
closed
1 year ago
0