remove ParameterSchedulers

.github/workflows/CI.yml

@@ -19,7 +19,7 @@ jobs:
       matrix:
         version:
           - '1.8'
-          - "~1.9.0-0"
+          - "1.9"
         os:
           - ubuntu-latest
         arch:

Project.toml

@@ -24,7 +24,6 @@ NNlibCUDA = "a00861dc-f156-4864-bf3c-e6376f28a68d"
 Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"
 Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
 OptimizationOptimisers = "42dfb2eb-d2b4-4451-abcd-913932933ac1"
-ParameterSchedulers = "d7d3b36b-41b8-4d0d-a2bf-768c6151755e"
 QuasiMonteCarlo = "8a4e6c94-4038-4cdc-81c3-7e6ffdb2a71b"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
@@ -64,12 +63,11 @@ NNlibCUDA = "0.2"
 Optimisers = "0.2"
 Optimization = "3"
 OptimizationOptimisers = "0.1"
-ParameterSchedulers = "0.3"
 QuasiMonteCarlo = "0.2, 0.3"
 Requires = "1"
 RuntimeGeneratedFunctions = "0.5"
 SciMLBase = "1"
-Sobol = "1"
+Sobol = "1, 2"
 StaticArrays = "1.5"
 StaticArraysCore = "1"
 StatsBase = "0.33, 0.34"

docs/make.jl

@@ -9,19 +9,13 @@ bib = CitationBibliography(joinpath(@__DIR__, "bibliography.bib"))
 
 DocMeta.setdocmeta!(Sophon, :DocTestSetup, :(using Sophon); recursive=true)
 
-makedocs(bib; modules=[Sophon], sitename="Sophon.jl",
+makedocs(; plugins=[bib], modules=[Sophon], sitename="Sophon.jl",
          repo="https://github.com/YichengDWu/Sophon.jl/blob/{commit}{path}#{line}",
          format=Documenter.HTML(; prettyurls=get(ENV, "CI", "false") == "true",
                                 canonical="https://YichengDWu.github.io/Sophon.jl",
                                 edit_link="main", assets=String[indigo]),
-         strict=[
-             :doctest,
-             :linkcheck,
-             :parse_error,
-             :example_block,
-             # Other available options are
-             # :autodocs_block, :cross_references, :docs_block, :eval_block, :example_block, :footnote, :meta_block, :missing_docs, :setup_block
-         ], authors="Yicheng Wu",
+         authors="Yicheng Wu",
+         warnonly = Documenter.except(:missing_docs)
          pages=[
              "Home" => "index.md",
              "Tutorials" => [

docs/src/tutorials/L_shape.md

@@ -3,7 +3,7 @@
 The example is taken from [here](https://arxiv.org/pdf/1907.04502.pdf). We showcase define a PDE on an L-shaped domain
 
 ```@example L
-using ModelingToolkit, DomainSets, Optimization, OptimizationOptimJL
+using ModelingToolkit, DomainSets, Optimization, OptimizationOptimJL, Zygote
 using DomainSets: ×
 using Sophon
 

docs/src/tutorials/SchrödingerEquation.md

@@ -7,7 +7,7 @@ $\mathrm{i} \partial_t \psi=-\frac{1}{2} \sigma \partial_{x x} \psi-\beta|\psi|^
 Let $\sigma=\beta=1, \psi=u+v i$, the equation can be transformed into a system of partial differential equations
 ```@example Schrödinger
 using ModelingToolkit, IntervalSets, Sophon, CairoMakie
-using Optimization, OptimizationOptimJL
+using Optimization, OptimizationOptimJL, Zygote
 
 @parameters x,t
 @variables u(..), v(..)

docs/src/tutorials/convection.md

@@ -13,7 +13,7 @@ First we define the PDE.
 
 ```@example convection
 using ModelingToolkit, Sophon, IntervalSets, CairoMakie
-using Optimization, OptimizationOptimJL
+using Optimization, OptimizationOptimJL, Zygote
 
 @parameters x, t
 @variables u(..)

docs/src/tutorials/discontinuous.md

@@ -133,7 +133,7 @@ savefig("result2.svg"); nothing # hide
 
 ## Quadratic activation function
 
-[`quadratic`](@ref) is much cheaper to compute compared to the Gaussian activation function.
+`quadratic` is much cheaper to compute compared to the Gaussian activation function.
 
 
 ```@example ds

docs/src/tutorials/helmholtz.md

@@ -12,10 +12,10 @@ where
 ```math 
 q(x, y)=-\left(a_{1} \pi\right)^{2} \sin \left(a_{1} \pi x\right) \sin \left(a_{2} \pi y\right)-\left(a_{2} \pi\right)^{2} \sin \left(a_{1} \pi x\right) \sin \left(a_{2} \pi y\right)+k^{2} \sin \left(a_{1} \pi x\right) \sin \left(a_{2} \pi y\right).
 ```
-The excat solution is ``u(x,y)=\sin{a_1\pi x}\sin{a_2\pi y}``. We chose ``k=1, a_1 = 1`` and ``a_2 = 4``.
+The exact solution is ``u(x,y)=\sin{a_1\pi x}\sin{a_2\pi y}``. We chose ``k=1, a_1 = 1`` and ``a_2 = 4``.
 
 ```@example helmholtz
-using ModelingToolkit, IntervalSets, Sophon, Lux
+using ModelingToolkit, IntervalSets, Sophon, Lux, Zygote
 using Optimization, OptimizationOptimJL
 
 @parameters x,y

docs/src/tutorials/hole.jl

@@ -1,6 +1,6 @@
 
 using ModelingToolkit, Sophon
-using Optimization, OptimizationOptimJL
+using Optimization, OptimizationOptimJL, Zygote
 using DomainSets
 using DomainSets: ×
 

docs/src/tutorials/inverse.md

@@ -10,7 +10,7 @@ $\frac{\mathrm{d} z}{\mathrm{~d} t}=x y-\beta z$,
 
 ```julia
 using ModelingToolkit, Sophon, OrdinaryDiffEq
-using Optimization, OptimizationOptimJL
+using Optimization, OptimizationOptimJL, Zygote
 using ModelingToolkit, IntervalSets
 ```
 

docs/src/tutorials/ode.md

@@ -5,7 +5,7 @@ This tutorial provides a step-by-step guide to solve the Lotka-Volterra system o
 ```@example ODE
 using ModelingToolkit
 using Sophon, IntervalSets
-using Optimization, OptimizationOptimJL
+using Optimization, OptimizationOptimJL, Zygote
 using Plots
 
 # Defining parameters and variables

docs/src/tutorials/poisson.md

@@ -8,7 +8,7 @@ u(x)=\sin (2 \pi x)+0.1 \sin (50 \pi x)
 
 ```@example poisson
 using ModelingToolkit, IntervalSets, Sophon
-using Optimization, OptimizationOptimJL
+using Optimization, OptimizationOptimJL, Zygote
 using CairoMakie
 
 @parameters x

docs/src/tutorials/sod.md

@@ -3,7 +3,7 @@
 This example showcases how to use adaptive weights in `Sophon`. 
 
 ```@example SOD
-using Optimization, OptimizationOptimJL, Plots
+using Optimization, OptimizationOptimJL, Plots, Zygote
 using ModelingToolkit, IntervalSets
 using Sophon
 using ChainRulesCore

docs/src/tutorials/waveinverse2.md

@@ -4,7 +4,7 @@ We are going to sovle the wave equation.
 
 ```@example wave
 using Sophon, ModelingToolkit, IntervalSets
-using Optimization, OptimizationOptimJL
+using Optimization, OptimizationOptimJL, Zygote
 
 @parameters x, t
 @variables u(..), c(..)

src/Sophon.jl

@@ -9,10 +9,6 @@ using Lux.WeightInitializers: _nfan
 import ModelingToolkit
 import ModelingToolkit: Differential
 using Optimization
-import ParameterSchedulers: Step, Exp, Poly, Inv, Triangle, TriangleDecay2, TriangleExp,
-                            Sin, SinDecay2, SinExp, CosAnneal, Sequence, Loop, Interpolator,
-                            Shifted, ComposedSchedule, Constant
-using ParameterSchedulers: AbstractSchedule
 using ComponentArrays
 import SciMLBase
 import SciMLBase: parameterless_type, __solve, build_solution, NullParameters