merge

Author: vyudu

docs/src/introduction_to_catalyst/catalyst_for_new_julia_users.md

@@ -55,7 +55,7 @@ To import a Julia package into a session, you can use the `using PackageName` co
 using Pkg
 Pkg.add("Catalyst")
 ```
-Here, the Julia package manager package (`Pkg`) is by default installed on your computer when Julia is installed, and can be activated directly. Next, we also wish to install the needed sub-libraries of `OrdinaryDiffEq` and `Plots` packages (for numeric simulation of models, and plotting, respectively). We will import the default recommended solver from the `OrdinaryDiffEqDefault` sub-library. A full list of `OrdinaryDiffEq` solver sublibraries can be found on the sidebar of [this page](https://docs.sciml.ai/OrdinaryDiffEq/stable/).
+Here, the Julia package manager package (`Pkg`) is by default installed on your computer when Julia is installed, and can be activated directly. Next, we install an ODE solver from a sub-library of the larger `OrdinaryDiffEq` package, and install the `Plots` package for making graphs. We will import the recommended default solver from the `OrdinaryDiffEqDefault` sub-library. A full list of `OrdinaryDiffEq` solver sublibraries can be found on the sidebar of [this page](https://docs.sciml.ai/OrdinaryDiffEq/stable/).
 ```julia
 Pkg.add("OrdinaryDiffEqDefault")
 Pkg.add("Plots")

docs/src/model_simulation/ode_simulation_performance.md

@@ -70,9 +70,8 @@ oprob = ODEProblem(bd_model, u0, tspan, ps)
 solve(oprob, Tsit5())
 nothing # hide
 ```
-
-If no solver argument is provided to `solve`, and the `OrdinaryDiffEqDefault` sub-library or top-level `OrdinaryDiffEq` library is installed, then one is automatically selected:
-```@example ode_simulation_performance_2
+If no solver argument is provided to `solve`, and the `OrdinaryDiffEqDefault` sub-library or meta `OrdinaryDiffEq` library are loaded, then one is automatically selected:
+```@example 
 using OrdinaryDiffEqDefault
 solve(oprob)
 nothing # hide
@@ -190,7 +189,7 @@ end
 u0 = [:X₁ => 2.0, :X₂ => 3.0]
 ps = [:k₁ => 1.0, :k₂ => 2.0]
 oprob = ODEProblem(rs, u0, (0.0, 10.0), ps; remove_conserved = true)
-sol = solve(oprob, Tsit5())
+sol = solve(oprob)
 nothing # hide
 ```
 Conservation law elimination is not expected to ever impact performance negatively; it simply results in a (possibly) lower-dimensional system of ODEs to solve. However, eliminating conserved species may have minimal performance benefits; it is model-dependent whether elimination results in faster ODE solving times and/or increased solution accuracy.

docs/src/steady_state_functionality/nonlinear_solve.md

@@ -102,7 +102,7 @@ Next, we provide these as an input to a `SteadyStateProblem`
 ssprob = SteadyStateProblem(dimer_production, u0, p)
 nothing # hide
 ```
-Finally, we can find the steady states using the `solver` command. Since `SteadyStateProblem`s are solved through forward ODE simulation, we must load the sublibrary of the [OrdinaryDiffEq.jl](https://github.com/SciML/OrdinaryDiffEq.jl) package that corresponds to the [selected ODE solver](@ref simulation_intro_solver_options). Any available ODE solver can be used, however, it has to be encapsulated by the `DynamicSS()` function. E.g. here we designate the `Rodas5P` solver and import the `OrdinaryDiffEqRosenbrock` sublibrary:
+Finally, we can find the steady states using the `solver` command. Since `SteadyStateProblem`s are solved through forward ODE simulation, we must load the sublibrary of the [OrdinaryDiffEq.jl](https://github.com/SciML/OrdinaryDiffEq.jl) package that corresponds to the [selected ODE solver](@ref simulation_intro_solver_options). Any available ODE solver can be used, however, it has to be encapsulated by the `DynamicSS()` function. E.g. here we use the `Rodas5P` solver which is loaded from the `OrdinaryDiffEqRosenbrock` sublibrary:
 
 (which requires loading the SteadyStateDiffEq package).
 ```@example steady_state_solving_simulation