Hi,

I’m trying to learn the basics of parameter estimation with DiffEqFlux by adapting the first example from here https://docs.juliahub.com/DiffEqFlux/BdO4p/1.9.0/ to my own problem. However, I keep getting the following error: `UndefVarError: DefaultOptimizationCache not defined`

. This occurs even when I copy and paste the example directly. It seems to be working fine until the last iteration when the error appears. The example is a couple years old, but it’s useful for my purposes. I would greatly appreciate if anyone can tell me what’s going on here and how to fix it. Thanks.

For convenience, here is the code from the example:

```
using DifferentialEquations, Flux, Optim, DiffEqFlux
function lotka_volterra(du,u,p,t)
x, y = u
α, β, δ, γ = p
du[1] = dx = α*x - β*x*y
du[2] = dy = -δ*y + γ*x*y
end
u0 = [1.0,1.0]
tspan = (0.0,10.0)
p = [1.5,1.0,3.0,1.0]
prob = ODEProblem(lotka_volterra,u0,tspan,p)
sol = solve(prob,Tsit5())
plot(sol)
function predict_adjoint(p) # Our 1-layer neural network
Array(concrete_solve(prob,Tsit5(),u0,p,saveat=0.0:0.1:10.0))
end
function loss_adjoint(p)
prediction = predict_adjoint(p)
loss = sum(abs2,x-1 for x in prediction)
loss,prediction
end
cb = function (p,l,pred) #callback function to observe training
display(l)
# using `remake` to re-create our `prob` with current parameters `p`
display(plot(solve(remake(prob,p=p),Tsit5(),saveat=0.0:0.1:10.0),ylim=(0,6)))
return false # Tell it to not halt the optimization. If return true, then optimization stops
end
# Display the ODE with the initial parameter values.
cb(p,loss_adjoint(p)...)
res = DiffEqFlux.sciml_train(loss_adjoint, p, BFGS(initial_stepnorm = 0.0001), cb = cb)[1]
plot(solve(remake(prob,p=res.minimizer),Tsit5(),saveat=0.0:0.1:10.0),ylim=(0,6))
And the stacktrace:
ERROR: UndefVarError: DefaultOptimizationCache not defined
Stacktrace:
[1] ___solve(prob::OptimizationProblem{true, OptimizationFunction{true, Optimization.AutoZygote, DiffEqFlux.var"#121#128"{typeof(loss_adjoint)}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, Vector{Float64}, SciMLBase.NullParameters, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Base.Iterators.Pairs{Union{}, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}}, opt::BFGS{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Nothing, Float64, Flat}, data::Base.Iterators.Cycle{Tuple{Optimization.NullData}}; callback::Function, maxiters::Nothing, maxtime::Nothing, abstol::Nothing, reltol::Nothing, progress::Bool, kwargs::Base.Iterators.Pairs{Union{}, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
@ OptimizationOptimJL C:\Users\alexa\.julia\packages\OptimizationOptimJL\WqQOV\src\OptimizationOptimJL.jl:168
[2] #__solve#2
@ C:\Users\alexa\.julia\packages\OptimizationOptimJL\WqQOV\src\OptimizationOptimJL.jl:67 [inlined]
[3] #solve#486
@ C:\Users\alexa\.julia\packages\SciMLBase\kTnku\src\solve.jl:89 [inlined]
[4] sciml_train(::typeof(loss_adjoint), ::Vector{Float64}, ::BFGS{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Nothing, Float64, Flat}, ::Nothing; lower_bounds::Nothing, upper_bounds::Nothing, cb::Function, callback::Function, maxiters::Nothing, kwargs::Base.Iterators.Pairs{Union{}, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
@ DiffEqFlux C:\Users\alexa\.julia\packages\DiffEqFlux\2IJEZ\src\train.jl:45
[5] top-level scope
@ c:\Users\alexa\Documents\Research\Chapter 2\Production_fit.jl:191
```