Add scalar dispatch for SymbolicNeuralNetwork

This commit adds a Number dispatch to StatelessApplyWrapper that allows
using scalar variables directly without wrapping them in arrays.

Previously, users had to write:
  sym_nn([X], θ)[1]

Now they can write:
  sym_nn(X, θ)[1]

The array syntax still works for backward compatibility.

Fixes #83

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

Author: ChrisRackauckas

src/ModelingToolkitNeuralNets.jl

@@ -121,6 +121,10 @@ function (wrapper::StatelessApplyWrapper)(input::AbstractArray, nn_p::AbstractVe
     stateless_apply(get_network(wrapper), input, convert(wrapper.T, nn_p))
 end
 
+function (wrapper::StatelessApplyWrapper)(input::Number, nn_p::AbstractVector)
+    wrapper([input], nn_p)
+end
+
 function Base.show(io::IO, m::MIME"text/plain", wrapper::StatelessApplyWrapper)
     printstyled(io, "LuxCore.stateless_apply wrapper for:\n", color = :gray)
     show(io, m, get_network(wrapper))

test/runtests.jl

@@ -6,4 +6,5 @@ using SafeTestsets
     @safetestset "QA" include("qa.jl")
     @safetestset "Basic" include("lotka_volterra.jl")
     @safetestset "MTK model macro compatibility" include("macro.jl")
+    @safetestset "Scalar dispatch" include("scalar_dispatch.jl")
 end

test/scalar_dispatch.jl

@@ -0,0 +1,69 @@
+using Test
+using ModelingToolkitNeuralNets
+using ModelingToolkit
+using Lux
+using StableRNGs
+using OrdinaryDiffEqVerner
+
+# Test scalar dispatch for SymbolicNeuralNetwork
+# This tests the fix for issue #83
+@testset "Scalar dispatch" begin
+    # Create a simple UDE with scalar inputs
+    @variables t X(t) Y(t)
+    @parameters d
+
+    chain = Lux.Chain(
+        Lux.Dense(1 => 3, Lux.softplus, use_bias = false),
+        Lux.Dense(3 => 3, Lux.softplus, use_bias = false),
+        Lux.Dense(3 => 1, Lux.softplus, use_bias = false)
+    )
+
+    sym_nn, θ = SymbolicNeuralNetwork(; nn_p_name = :θ, chain, n_input = 1, n_output = 1, rng = StableRNG(42))
+
+    # Test that scalar dispatch works (fix for issue #83)
+    # Previously required: sym_nn([Y], θ)[1]
+    # Now can use: sym_nn(Y, θ)[1]
+    Dt = ModelingToolkit.D_nounits
+    eqs_ude = [
+        Dt(X) ~ sym_nn(Y, θ)[1] - d*X,
+        Dt(Y) ~ X - d*Y
+    ]
+
+    @named sys = System(eqs_ude, ModelingToolkit.t_nounits)
+    sys_compiled = mtkcompile(sys)
+
+    # Test that the system can be created and solved
+    prob = ODEProblem{true, SciMLBase.FullSpecialize}(
+        sys_compiled,
+        [X => 1.0, Y => 1.0],
+        (0.0, 1.0),
+        [d => 0.1]
+    )
+
+    sol = solve(prob, Vern9(), abstol = 1e-8, reltol = 1e-8)
+
+    @test SciMLBase.successful_retcode(sol)
+
+    # Also test that the old array syntax still works
+    eqs_ude_old = [
+        Dt(X) ~ sym_nn([Y], θ)[1] - d*X,
+        Dt(Y) ~ X - d*Y
+    ]
+
+    @named sys_old = System(eqs_ude_old, ModelingToolkit.t_nounits)
+    sys_old_compiled = mtkcompile(sys_old)
+
+    prob_old = ODEProblem{true, SciMLBase.FullSpecialize}(
+        sys_old_compiled,
+        [X => 1.0, Y => 1.0],
+        (0.0, 1.0),
+        [d => 0.1]
+    )
+
+    sol_old = solve(prob_old, Vern9(), abstol = 1e-8, reltol = 1e-8)
+
+    @test SciMLBase.successful_retcode(sol_old)
+
+    # Both solutions should be the same
+    @test sol.u ≈ sol_old.u
+end