update Doc

SciML · Oct 10, 2024 · 526fd0c · 526fd0c
1 parent f59f225
commit 526fd0c
Show file tree

Hide file tree

Showing 3 changed files with 10 additions and 11 deletions.
diff --git a/docs/Project.toml b/docs/Project.toml
@@ -29,21 +29,21 @@ SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
 
 [compat]
 AdvancedHMC = "0.6.1"
-ComponentArrays = "0.15.8"
+ComponentArrays = "0.15.16"
 Cubature = "1.5"
 DiffEqBase = "6.148"
 Distributions = "0.25.107"
 Documenter = "1"
 DomainSets = "0.6, 0.7"
-Flux = "0.14.11"
-Integrals = "4"
+Flux = "0.14.17"
+Integrals = "4.5"
 LineSearches = "7.2"
-Lux = "0.5.22"
-LuxCUDA = "0.3.2"
+Lux = "1.1"
+LuxCUDA = "0.3"
 MethodOfLines = "0.11"
-ModelingToolkit = "9.7"
+ModelingToolkit = "9.9"
 MonteCarloMeasurements = "1"
-NeuralPDE = "5.14"
+NeuralPDE = "5.16"
 Optimization = "3.24, 4"
 OptimizationOptimJL = "0.2.1, 0.3, 0.4"
 OptimizationOptimisers = "0.2.1, 0.3"

diff --git a/docs/src/tutorials/pino_ode.md b/docs/src/tutorials/pino_ode.md
@@ -7,7 +7,7 @@ This tutorial provides an example of how to use the Physics Informed Neural Oper
 In this section, we will define a parametric ODE and then learn it with a PINO using [`PINOODE`](@ref). The PINO will be trained to learn the mapping from the parameters of the ODE to its solution.
 
 ```@example pino
-using Test # hide
+using Test
 using OptimizationOptimisers
 using Lux
 using Statistics, Random
@@ -33,12 +33,12 @@ deeponet = NeuralOperators.DeepONet(
 bounds = [(1.0, pi), (1.0, 2.0), (2.0, 3.0)]
 number_of_parameter_samples = 50
 # Define the training strategy
-strategy = StochasticTraining(60)
+strategy = StochasticTraining(20)
 # Define the optimizer
 opt = OptimizationOptimisers.Adam(0.03)
 alg = PINOODE(deeponet, opt, bounds, number_of_parameters; strategy = strategy)
 # Solve the ODE problem using the PINOODE algorithm
-sol = solve(prob, alg, verbose = false, maxiters = 3000)
+sol = solve(prob, alg, verbose = false, maxiters = 4000)
 ```
 
 Now let's compare the prediction from the learned operator with the ground truth solution which is obtained by analytic solution of the parametric ODE.

diff --git a/src/NeuralPDE.jl b/src/NeuralPDE.jl
@@ -34,7 +34,6 @@ import ChainRulesCore, Lux, ComponentArrays
 using Lux: FromFluxAdaptor, recursive_eltype
 using ChainRulesCore: @non_differentiable
 using NeuralOperators
-import Lux: Chain
 import NeuralOperators: DeepONet
 RuntimeGeneratedFunctions.init(@__MODULE__)