docs: update docs

Author: SebastianM-C

docs/Project.toml

@@ -15,8 +15,8 @@ SymbolicIndexingInterface = "2efcf032-c050-4f8e-a9bb-153293bab1f5"
 [compat]
 Documenter = "1.3"
 Lux = "1"
-ModelingToolkit = "9.9"
-ModelingToolkitNeuralNets = "1"
+ModelingToolkit = "10"
+ModelingToolkitNeuralNets = "2"
 ModelingToolkitStandardLibrary = "2.7"
 Optimization = "3.24, 4.0"
 OptimizationOptimisers = "0.2.1, 0.3"
@@ -27,4 +27,4 @@ StableRNGs = "1"
 SymbolicIndexingInterface = "0.3.15"
 
 [sources]
-ModelingToolkitNeuralNets = { path = ".." }
+ModelingToolkitNeuralNets = {path = ".."}

docs/src/friction.md

@@ -49,7 +49,7 @@ function friction_true()
     eqs = [
         Dt(y) ~ Fu - friction(y)
     ]
-    return ODESystem(eqs, t, name = :friction_true)
+    return System(eqs, t, name = :friction_true)
 end
 ```
 
@@ -58,7 +58,7 @@ Now that we have defined the model, we will simulate it from 0 to 0.1 seconds.
 ```@example friction
 model_true = structural_simplify(friction_true())
 prob_true = ODEProblem(model_true, [], (0, 0.1), [])
-sol_ref = solve(prob_true, Rodas4(); saveat = 0.001)
+sol_ref = solve(prob_true, Vern7(); saveat = 0.001)
 ```
 
 Let's plot it.
@@ -85,7 +85,7 @@ function friction_ude(Fu)
     @named nn_out = RealOutputArray(nout = 1)
     eqs = [Dt(y) ~ Fu - nn_in.u[1]
            y ~ nn_out.u[1]]
-    return ODESystem(eqs, t, name = :friction, systems = [nn_in, nn_out])
+    return System(eqs, t, name = :friction, systems = [nn_in, nn_out])
 end
 
 Fu = 120.0
@@ -98,8 +98,8 @@ chain = Lux.Chain(
 )
 @named nn = NeuralNetworkBlock(1, 1; chain = chain, rng = StableRNG(1111))
 
-eqs = [connect(model.nn_in, nn.output)
-       connect(model.nn_out, nn.input)]
+eqs = [connect(model.nn_in.u, nn.output)
+       connect(model.nn_out.u, nn.input)]
 
 ude_sys = complete(ODESystem(eqs, t, systems = [model, nn], name = :ude_sys))
 sys = structural_simplify(ude_sys)
@@ -114,7 +114,7 @@ function loss(x, (prob, sol_ref, get_vars, get_refs, set_x))
     new_p = set_x(prob, x)
     new_prob = remake(prob, p = new_p, u0 = eltype(x).(prob.u0))
     ts = sol_ref.t
-    new_sol = solve(new_prob, Rodas4(), saveat = ts, abstol = 1e-8, reltol = 1e-8)
+    new_sol = solve(new_prob, Vern7(), saveat = ts, abstol = 1e-8, reltol = 1e-8)
     loss = zero(eltype(x))
     for i in eachindex(new_sol.u)
         loss += sum(abs2.(get_vars(new_sol, i) .- get_refs(sol_ref, i)))
@@ -126,7 +126,7 @@ function loss(x, (prob, sol_ref, get_vars, get_refs, set_x))
     end
 end
 
-of = OptimizationFunction{true}(loss, AutoForwardDiff())
+of = OptimizationFunction(loss, AutoForwardDiff())
 
 prob = ODEProblem(sys, [], (0, 0.1), [])
 get_vars = getu(sys, [sys.friction.y])

docs/src/index.md

@@ -23,7 +23,7 @@ Pkg.add("ModelingToolkitNeuralNets")
 
   - See the [SciML Style Guide](https://github.com/SciML/SciMLStyle) for common coding practices and other style decisions.
   - There are a few community forums:
-    
+
       + The #diffeq-bridged and #sciml-bridged channels in the
         [Julia Slack](https://julialang.org/slack/)
       + The #diffeq-bridged and #sciml-bridged channels in the