diff --git a/src/core/control.jl b/src/core/control.jl
index 10d2fa5..4a6df06 100644
--- a/src/core/control.jl
+++ b/src/core/control.jl
@@ -29,7 +29,7 @@ function ComputeGradient(∂J_∂α::Vector{Float64}, α::Vector{Float64}, femmo
 	# zero ALL depth of the model, make sure we get correct gradient
 	dfemmodel = Enzyme.Compiler.make_zero(Base.Core.Typeof(femmodel), IdDict(), femmodel)
 	# compute the gradient
-	autodiff(Enzyme.Reverse, costfunction, Active, Duplicated(α, ∂J_∂α), Duplicated(femmodel,dfemmodel))
+	autodiff(set_runtime_activity(Enzyme.Reverse), costfunction, Active, Duplicated(α, ∂J_∂α), Duplicated(femmodel,dfemmodel))
 end#}}}
 function CostFunctionx(femmodel::FemModel, α::Vector{Float64}, controlvar_enum::IssmEnum, SId_enum::IssmEnum, cost_enum_list::Vector{IssmEnum}, ::Val{solutionstring}) where solutionstring #{{{
 	#Update FemModel accordingly
diff --git a/test/testoptimization.jl b/test/testoptimization.jl
index 25b35f9..55c9c6f 100755
--- a/test/testoptimization.jl
+++ b/test/testoptimization.jl
@@ -29,14 +29,15 @@ md.inversion.dependent_string = ["SurfaceAbsVelMisfit"]
 femmodel=DJUICE.ModelProcessor(md, :StressbalanceSolution)
 n = length(α)
 
+DJUICE.costfunction(α, femmodel)
 # test Enzyme autodiff only
 dfemmodel = Enzyme.Compiler.make_zero(Base.Core.Typeof(femmodel), IdDict(), femmodel)
-autodiff(Enzyme.Reverse, DJUICE.costfunction, Active, Duplicated(α, ∂J_∂α), Duplicated(femmodel,dfemmodel))
+autodiff(set_runtime_activity(Enzyme.Reverse), DJUICE.costfunction, Active, Duplicated(α, ∂J_∂α), Duplicated(femmodel,dfemmodel))
 
 # use user defined grad, errors!
-optprob = OptimizationFunction(DJUICE.costfunction, Optimization.AutoEnzyme())
+#optprob = OptimizationFunction(DJUICE.costfunction, Optimization.AutoEnzyme())
 #prob = Optimization.OptimizationProblem(optprob, α, femmodel, lb=md.inversion.min_parameters, ub=md.inversion.max_parameters)
-prob = Optimization.OptimizationProblem(optprob, α, femmodel)
-sol = Optimization.solve(prob,  Optimization.LBFGS())
+#prob = Optimization.OptimizationProblem(optprob, α, femmodel)
+#sol = Optimization.solve(prob,  Optimization.LBFGS())
 #sol = Optimization.solve(prob, Optim.GradientDescent())
 #sol = Optimization.solve(prob, Optim.NelderMead())