byuflowlab/ImplicitAD.jl

Automates steady and unsteady adjoints (general solvers and ODEs respectively). 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.

语言：Julia · GitHub 星标：29 · Fork：10

话题：algorithmic-differentiation · automatic-differentiation · implicit-functions

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