Just Float64

docs/src/examples/ode/exogenous_input.md

@@ -45,10 +45,10 @@ using OrdinaryDiffEq, Lux, ComponentArrays, Optimization,
       OptimizationPolyalgorithms, OptimizationOptimisers, Plots, Random
 
 rng = Random.default_rng()
-tspan = (0.1f0, Float32(10.0))
+tspan = (0.1, 10.0)
 tsteps = range(tspan[1], tspan[2], length = 100)
 t_vec = collect(tsteps)
-ex = vec(ones(Float32, length(tsteps), 1))
+ex = vec(ones(Float64, length(tsteps), 1))
 f(x) = (atan(8.0 * x - 4.0) + atan(4.0)) / (2.0 * atan(4.0))
 
 function hammerstein_system(u)
@@ -59,13 +59,13 @@ function hammerstein_system(u)
     return y
 end
 
-y = Float32.(hammerstein_system(ex))
+y = hammerstein_system(ex)
 plot(collect(tsteps), y, ticks = :native)
 
 nn_model = Lux.Chain(Lux.Dense(2, 8, tanh), Lux.Dense(8, 1))
 p_model, st = Lux.setup(rng, nn_model)
 
-u0 = Float32.([0.0])
+u0 = Float64.([0.0])
 
 function dudt(u, p, t)
     global st
@@ -89,7 +89,7 @@ end
 
 adtype = Optimization.AutoZygote()
 optf = Optimization.OptimizationFunction((x, p) -> loss(x), adtype)
-optprob = Optimization.OptimizationProblem(optf, ComponentArray(p_model))
+optprob = Optimization.OptimizationProblem(optf, ComponentArray{Float64}(p_model))
 
 res0 = Optimization.solve(optprob, PolyOpt(), maxiters = 100)