1- using ModelingToolkit, OrdinaryDiffEq, RecursiveArrayTools, SymbolicIndexingInterface, Test
1+ using ModelingToolkit, OrdinaryDiffEq, RecursiveArrayTools, SymbolicIndexingInterface, Zygote, Test
22using Optimization, OptimizationOptimJL
33using ModelingToolkit: t_nounits as t, D_nounits as D
44
@@ -107,7 +107,7 @@ true_grad_sym[idx_sym] .= 1.
107107@test " Symbolic Indexing Adjoint: Symbol" all (x -> x == true_grad_sym, gs_sym)
108108
109109gs_vec, = Zygote. gradient (sol) do sol
110- sum (sum .(sol[[lorenz1. x, lorenz2]]))
110+ sum (sum .(sol[[lorenz1. x, lorenz2. x ]]))
111111end
112112idx_vecsym = SymbolicIndexingInterface. variable_index .(Ref (sys), [lorenz1. x, lorenz2. x])
113113true_grad_vecsym = zeros (length (ModelingToolkit. unknowns (sys)))
@@ -116,7 +116,7 @@ true_grad_vecsym[idx_vecsym] .= 1.
116116@test " Symbolic Indexing Adjoint: Vector{Symbol}" all (x -> x == true_grad_vecsym, gs_vec)
117117
118118gs_tup, = Zygote. gradient (sol) do sol
119- sum (sum .(collect .(sol[(lorenz1. x, lorenz2)])))
119+ sum (sum .(collect .(sol[(lorenz1. x, lorenz2. x )])))
120120end
121121idx_tupsym = SymbolicIndexingInterface. variable_index .(Ref (sys), [lorenz1. x, lorenz2. x])
122122true_grad_tupsym = zeros (length (ModelingToolkit. unknowns (sys)))
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