remove running

docs/src/optimal_control/SDE_control.md

@@ -17,7 +17,7 @@ to ultimately prepare and stabilize the qubit in the excited state.
 Before getting to the explanation, here's some code to start with. We will
 follow a full explanation of the definition and training process:
 
-```@example
+```julia
 # load packages
 using DiffEqFlux
 using DiffEqSensitivity
@@ -302,7 +302,7 @@ visualization_callback(res.u, loss(res.u); doplot=true)
 ## Step-by-step description
 
 ### Load packages
-```@example sdecontrol
+```julia
 using DiffEqFlux
 using DiffEqSensitivity
 using Optimization
@@ -313,7 +313,7 @@ using Plots
 
 ### Parameters
 We define the parameters of the qubit and hyper-parameters of the training process.
-```@example sdecontrol
+```julia
 lr = 0.01f0
 epochs = 100
 
@@ -377,7 +377,7 @@ In plain terms, the quantities that were defined are:
 We use a neural network to control the parameter Ω(t). Alternatively, one could
 also, e.g., use [tensor layers](https://diffeqflux.sciml.ai/dev/layers/TensorLayer/), Flux.jl, or Lux.jl.
 
-```@example sdecontrol
+```julia
 # state-aware
 nn = FastChain(
   FastDense(4, 32, relu),
@@ -394,7 +394,7 @@ To avoid complex numbers in our simulations, we split the state of the qubit
 ```
 into its real and imaginary part.
 
-```@example sdecontrol
+```julia
 # initial state anywhere on the Bloch sphere
 function prepare_initial(dt, n_par)
   # shape 4 x n_par
@@ -422,7 +422,7 @@ package, as one possibility to simulate a 1D Brownian motion. Note that the NN
 is placed directly into the drift function, thus the control parameter Ω is
 continuously updated.
 
-```@example sdecontrol
+```julia
 # Define SDE
 function qubit_drift!(du,u,p,t)
   # expansion coefficients |Ψ> = ce |e> + cd |d>
@@ -495,7 +495,7 @@ parallel ensemble simulation docs](https://diffeq.sciml.ai/latest/features/ensem
 for a description of the available ensemble algorithms. To optimize only the parameters
 of the neural network, we use `pars = [p; myparameters.Δ; myparameters.Ωmax; myparameters.κ]`
 
-``` @example sdecontrol
+``` julia
 # compute loss
 function g(u,p,t)
   ceR = @view u[1,:,:]
@@ -549,7 +549,7 @@ To visualize the performance of the controller, we plot the mean value and
 standard deviation of the fidelity of a bunch of trajectories (`myparameters.numtrajplot`) as
 a function of the time steps at which loss values are computed.
 
-```@example sdecontrol
+```julia
 function visualize(p; alg=EM())
 
   u0 = prepare_initial(myparameters.dt, myparameters.numtrajplot)
@@ -628,7 +628,7 @@ of the neural network.
 sensitivity methods. The necessary correction between Ito and Stratonovich integrals
 is computed under the hood in the DiffEqSensitivity package.
 
-```@example sdecontrol
+```julia
 # optimize the parameters for a few epochs with ADAM on time span
 # Setup and run the optimization
 adtype = Optimization.AutoZygote()