Use OrdinaryDiffEqRosenbrock.jl (#108)

* use OrdinaryDiffEqRosenbrock

* add compat bound for OrdinaryDiffEqRosenbrock

* bump compat of SciMLBase

* also use OrdinaryDiffEqNonlinearSolve

* document need of OrdinaryDiffEqNonlinearSolve.jl

* fix typo

* import OrdinaryDiffEqNonlinearSolve in tests

* bump compat bounds of OrdinaryDiffEq*

* run Downgrade on Julia 1.11

Author: JoshuaLampert

.github/workflows/Downgrade.yml

@@ -33,7 +33,7 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.10'
+          - '1.11'
           # - '~1.9.0-0' # including development versions
           # - 'nightly'
         os:

Project.toml

@@ -37,7 +37,7 @@ Random = "1"
 ReadVTK = "0.2"
 RecipesBase = "1.3.4"
 Reexport = "1.2"
-SciMLBase = "2.26"
+SciMLBase = "2.56"
 SimpleUnPack = "1.1"
 SpecialFunctions = "2"
 StaticArrays = "1.9"

README.md

@@ -38,7 +38,8 @@ julia> Pkg.add("Plots")
 
 To create special node sets, you might also want to install [QuasiMonteCarlo.jl](https://github.com/SciML/QuasiMonteCarlo.jl) or
 [Meshes.jl](https://github.com/JuliaGeometry/Meshes.jl) and for solving time-dependent partial differential equations
-[OrdinaryDiffEq.jl](https://github.com/SciML/OrdinaryDiffEq.jl) in a similar way as above for Plots.jl. See the documentation for more
+[OrdinaryDiffEq.jl](https://github.com/SciML/OrdinaryDiffEq.jl) in a similar way as above for Plots.jl. Consider using the subpackage
+OrdinaryDiffEqRosenbrock.jl as it contains the most relevant time integration schemes for this package. See the documentation for more
 examples on how to use these packages in combination with KernelInterpolation.jl.
 
 ## Usage

docs/src/development.md

@@ -19,7 +19,7 @@ cd KernelInterpolation.jl
 mkdir run
 cd run
 julia --project=. -e 'using Pkg; Pkg.develop(PackageSpec(path=".."))' # Install local KernelInterpolation.jl clone
-julia --project=. -e 'using Pkg; Pkg.add(["Plots", "QuasiMonteCarlo", "Meshes", "OrdinaryDiffEq"])' # Install additional packages
+julia --project=. -e 'using Pkg; Pkg.add(["Plots", "QuasiMonteCarlo", "Meshes", "OrdinaryDiffEqRosenbrock", "OrdinaryDiffEqNonlinearSolve"])' # Install additional packages
 ```
 
 If you use other packages for executing KernelInterpolation.jl, you can add them to the project in the `run`

docs/src/index.md

@@ -38,7 +38,8 @@ julia> Pkg.add("Plots")
 
 To create special node sets, you might also want to install [QuasiMonteCarlo.jl](https://github.com/SciML/QuasiMonteCarlo.jl) or
 [Meshes.jl](https://github.com/JuliaGeometry/Meshes.jl) and for solving time-dependent partial differential equations
-[OrdinaryDiffEq.jl](https://github.com/SciML/OrdinaryDiffEq.jl) in a similar way as above for Plots.jl. See the documentation for more
+[OrdinaryDiffEq.jl](https://github.com/SciML/OrdinaryDiffEq.jl) in a similar way as above for Plots.jl. Consider using the subpackage
+OrdinaryDiffEqRosenbrock.jl as it contains the most relevant time integration schemes for this package. See the documentation for more
 examples on how to use these packages in combination with KernelInterpolation.jl.
 
 ## Usage

docs/src/pdes.md

@@ -226,6 +226,9 @@ which already provides a wide range of time integration methods. Note that this
 system, which is more difficult to solve than a simple ODE system. Thus, we are restricted to specialized time integration
 methods, which can handle DAEs. We recommend using the `Rodas5P` method, which is a Rosenbrock method for stiff DAEs. See
 also the [documentation of OrdinaryDiffEq.jl](https://docs.sciml.ai/DiffEqDocs/latest/tutorials/dae_example/) for more information.
+`Rodas5P` along with other Rosenbrock methods are implemented in the subpackage OrdinaryDiffEqRosenbrock.jl. Therefore, we
+recommend using this package in combination with KernelInterpolation.jl to reduce precompile time. In order to solve DAEs with
+OrdinaryDiffEqRosenbrock.jl, we also need to import the subpackage OrdinaryDiffEqNonlinearSolve.jl.
 
 In KernelInterpolation.jl, you can use the constructor of a [`Semidiscretization`](@ref) in a very similar way as
 [`SpatialDiscretization`](@ref), but with the additional initial condition. This can be turned into an `ODEProblem` object

examples/PDEs/advection_1d_basic.jl

@@ -1,5 +1,5 @@
 using KernelInterpolation
-using OrdinaryDiffEq
+using OrdinaryDiffEqRosenbrock, OrdinaryDiffEqNonlinearSolve
 using Plots
 
 # source term of advection equation

examples/PDEs/advection_3d_basic.jl

@@ -1,5 +1,5 @@
 using KernelInterpolation
-using OrdinaryDiffEq
+using OrdinaryDiffEqRosenbrock, OrdinaryDiffEqNonlinearSolve
 using LinearAlgebra: norm
 using WriteVTK: WriteVTK, paraview_collection
 

examples/PDEs/advection_diffusion_2d_basic.jl

@@ -1,5 +1,5 @@
 using KernelInterpolation
-using OrdinaryDiffEq
+using OrdinaryDiffEqRosenbrock, OrdinaryDiffEqNonlinearSolve
 using LinearAlgebra: norm
 using Plots
 

examples/PDEs/heat_2d_basic.jl

@@ -1,5 +1,5 @@
 using KernelInterpolation
-using OrdinaryDiffEq
+using OrdinaryDiffEqRosenbrock, OrdinaryDiffEqNonlinearSolve
 using Plots
 
 # right-hand-side of Heat equation