Hi, I am using DifferentialEquations with callbacks to solve a differential equation with parameters that change at some point in time. The solution works fine, it is taken directly from the documentation for DifferentialEquations. Now I want to calculate the derivative of the obtained solution with respect to the initial conditions. Following the example from https://diffeq.sciml.ai/dev/analysis/sensitivity/#concrete_solve-Examples-1 I made a function solutionSim that takes only the initial condition. The issue is with the SimType structure:

LoadError: MethodError: no method matching SimType(::Array{ForwardDiff.Dual{ForwardDiff.Tag{typeof(solutionSim),Float64},Float64,2},2}, ::Float64)

Closest candidates are:

SimType(!Matched::Array{T,1}, ::T) where T

How can I adjust SimType so it is able to accept dual numbers?

MWE:

```
using DiffEqSensitivity, DifferentialEquations, ForwardDiff
mutable struct SimType{T} <: DEDataVector{T}
x::Array{T,1}
f1::T
end
function f(du,u,p,t)
du[1] = -0.5*u[1] + u.f1
du[2] = -0.5*u[2]
end
const tstop1 = [5.]
const tstop2 = [8.]
function condition(u,t,integrator)
t in tstop1
end
function condition2(u,t,integrator)
t in tstop2
end
function affect!(integrator)
for c in full_cache(integrator)
c.f1 = 1.5
end
end
function affect2!(integrator)
for c in full_cache(integrator)
c.f1 = -1.5
end
end
save_positions = (true,true)
cb = DiscreteCallback(condition, affect!, save_positions=save_positions)
save_positions = (false,true)
cb2 = DiscreteCallback(condition2, affect2!, save_positions=save_positions)
cbs = CallbackSet(cb,cb2)
u0 = SimType([10.5;1.0], 0.0)
prob = ODEProblem(f,u0,(0.0,10.0))
const tstop = [5.;8.]
sol = DifferentialEquations.solve(prob,Tsit5(),callback = cbs, tstops=tstop)
#####Does not work from here
function solutionSim(x)
u0=SimType(x,0.0)
_prob = remake(prob,[u0])
DifferentialEquations.solve(_prob,Tsit5(),callback = cbs, tstops=tstop)[end]
end
# #
out=zeros(2,2)
dxJac = ForwardDiff.jacobian!(out,solutionSim,[10.0 10.0])
```