get rid of the numbered list

docs/src/time_integration.md

@@ -81,15 +81,15 @@ Nevertheless, due to their optimized stability properties and low-storage nature
 
 In this tutorial, we will demonstrate how you can use the second-order PERK time integrator. You need the packages `Convex.jl` and `ECOS.jl`, so be sure they are added to your environment.
 
-1. First, you need to load the necessary packages:
+First, you need to load the necessary packages:
 
 ```@example PERK-example-1
 using Convex, ECOS
 using OrdinaryDiffEq
 using Trixi
 ```
 
-2. Define the ODE problem and the semidiscretization setup. For this example, we will use a simple advection problem.
+Then, define the ODE problem and the semidiscretization setup. For this example, we will use a simple advection problem.
 
 ```@example PERK-example-1
 # Define the mesh
@@ -114,7 +114,7 @@ semi = SemidiscretizationHyperbolic(mesh,
                                     solver)
 ```
 
-3. Define the necessary [callbacks](@ref callbacks-id) for the simulation. Callbacks are used to perform actions at specific points during the integration process.
+After that, we will define the necessary [callbacks](@ref callbacks-id) for the simulation. Callbacks are used to perform actions at specific points during the integration process.
 
 ```@example PERK-example-1
 # Define some standard callbacks
@@ -131,7 +131,7 @@ callbacks = CallbackSet(summary_callback,
                         stepsize_callback)
 ```
 
-4. Define the ODE problem by specifying the time span over which the ODE will be solved. 
+Now, we define the ODE problem by specifying the time span over which the ODE will be solved. 
 The `tspan` parameter is a tuple `(t_start, t_end)` that defines the start and end times for the simulation. 
 The `semidiscretize` function is used to create the ODE problem from the simulation setup.
 
@@ -143,7 +143,7 @@ tspan = (0.0, 1.0)
 ode = semidiscretize(semi, tspan)
 ```
 
-5. In this step we will construct the time integrator. In order to do this, you need the following components:
+Next, we will construct the time integrator. In order to do this, you need the following components:
 
   - Number of stages: The number of stages $S$ in the Runge-Kutta method. 
   In this example, we use `6` stages.
@@ -161,7 +161,7 @@ ode = semidiscretize(semi, tspan)
 ode_algorithm = Trixi.PairedExplicitRK2(6, tspan, semi)
 ```
 
-6. With everything now set up, you can now use `Trixi.solve` to solve the ODE problem. The `solve` function takes the ODE problem, the time integrator, and some options such as the time step (`dt`), whether to save every step (`save_everystep`), and the callbacks.
+With everything now set up, you can now use `Trixi.solve` to solve the ODE problem. The `solve` function takes the ODE problem, the time integrator, and some options such as the time step (`dt`), whether to save every step (`save_everystep`), and the callbacks.
 
 ```@example PERK-example-1
 # Solve the ODE problem using PERK2
@@ -170,7 +170,7 @@ sol = Trixi.solve(ode, ode_algorithm,
                   save_everystep = false, callback = callbacks)
 ```
 
-7. Advanced constructors:
+##### Advanced constructors:
 There are two additional constructors for the `PairedExplicitRK2` method besides the one taking in a semidiscretization `semi`:
   - `PairedExplicitRK2(num_stages, base_path_monomial_coeffs::AbstractString)` constructs a `num_stages`-stage method from the given optimal monomial_coeffs $\boldsymbol \alpha$.
   These are expected to be present in the provided directory in the form of a `gamma_<S>.txt` file, where `<S>` is the number of stages `num_stages`.