Added Boltz.jl to docs examples and fixed errors building documenta…

…tion
SciML · Sep 11, 2024 · a12ed2c · a12ed2c
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Showing 3 changed files with 6 additions and 9 deletions.
diff --git a/docs/Project.toml b/docs/Project.toml
@@ -1,4 +1,5 @@
 [deps]
+Boltz = "4544d5e4-abc5-4dea-817f-29e4c205d9c8"
 DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 Lux = "b2108857-7c20-44ae-9111-449ecde12c47"

diff --git a/docs/src/demos/damped_SHO.md b/docs/src/demos/damped_SHO.md
@@ -88,8 +88,6 @@ prob = discretize(pde_system, discretization)
 ########################## Solve OptimizationProblem ##########################
 
 res = Optimization.solve(prob, OptimizationOptimisers.Adam(); maxiters = 500)
-prob = Optimization.remake(prob, u0 = res.u)
-res = Optimization.solve(prob, OptimizationOptimJL.BFGS(); maxiters = 500)
 
 ###################### Get numerical numerical functions ######################
 net = discretization.phi
@@ -206,8 +204,6 @@ prob = discretize(pde_system, discretization)
 using Optimization, OptimizationOptimisers, OptimizationOptimJL
 
 res = Optimization.solve(prob, OptimizationOptimisers.Adam(); maxiters = 500)
-prob = Optimization.remake(prob, u0 = res.u)
-res = Optimization.solve(prob, OptimizationOptimJL.BFGS(); maxiters = 500)
 
 net = discretization.phi
 θ = res.u.depvar

diff --git a/docs/src/demos/policy_search.md b/docs/src/demos/policy_search.md
@@ -13,7 +13,7 @@ We'll jointly train a neural controller ``\tau = u \left( \theta, \frac{d\theta}
 ## Copy-Pastable Code
 
 ```julia
-using NeuralPDE, Lux, ModelingToolkit, NeuralLyapunov
+using NeuralPDE, Lux, Boltz, ModelingToolkit, NeuralLyapunov
 import Optimization, OptimizationOptimisers, OptimizationOptimJL
 using Random
 
@@ -55,7 +55,7 @@ dim_phi = 3
 dim_u = 1
 dim_output = dim_phi + dim_u
 chain = [Lux.Chain(
- PeriodicEmbedding([1], [2π]),
+ Boltz.Layers.PeriodicEmbedding([1], [2π]),
  Dense(3, dim_hidden, tanh),
  Dense(dim_hidden, dim_hidden, tanh),
  Dense(dim_hidden, 1)
@@ -179,7 +179,7 @@ Other than that, setting up the neural network using Lux and NeuralPDE training
 For more on that aspect, see the [NeuralPDE documentation](https://docs.sciml.ai/NeuralPDE/stable/).
 
 ```@example policy_search
-using Lux
+using Lux, Boltz
 
 # Define neural network discretization
 # We use an input layer that is periodic with period 2π with respect to θ
@@ -189,7 +189,7 @@ dim_phi = 3
 dim_u = 1
 dim_output = dim_phi + dim_u
 chain = [Lux.Chain(
- PeriodicEmbedding([1], [2π]),
+ Boltz.Layers.PeriodicEmbedding([1], [2π]),
  Dense(3, dim_hidden, tanh),
  Dense(dim_hidden, dim_hidden, tanh),
  Dense(dim_hidden, 1)
@@ -384,7 +384,7 @@ Now, let's simulate the closed-loop dynamics to verify that the controller can g
 First, we'll start at the downward equilibrium:
 
 ```@example policy_search
-state_order = map(st -> SymbolicUtils.iscall(st) ? operation(st) : st, state_order)
+state_order = map(st -> SymbolicUtils.isterm(st) ? operation(st) : st, state_order)
 state_syms = Symbol.(state_order)
 
 closed_loop_dynamics = ODEFunction(