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SciML · Sep 4, 2024 · 38aca03 · 38aca03
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diff --git a/docs/src/basics/solution.md b/docs/src/basics/solution.md
@@ -191,9 +191,10 @@ Extra fields for solutions of specific problems are specified in the appropriate
 problem definition page.
 
 ## Solution Function Stripping
-By default solution objects store functions, making them difficult to serialize. Using the function 
+
+By default solution objects store functions, making them difficult to serialize. Using the function
 `strip_solution(sol)`, a copy of the solution that does not contain any functions is created.
 
 ```@docs
 SciMLBase.strip_solution
-```
+```
diff --git a/docs/src/tutorials/advanced_ode_example.md b/docs/src/tutorials/advanced_ode_example.md
@@ -11,15 +11,15 @@ equations (PDEs). For example, we will use the stiff Brusselator partial
 differential equation (BRUSS).
 
 !!! note
-
+    
     This tutorial is for advanced users to dive into advanced features!
     DifferentialEquations.jl automates most of this usage, so we recommend
     users try `solve(prob)` with the automatic algorithm first!
 
 ## Definition of the Brusselator Equation
 
 !!! note
-
+    
     Feel free to skip this section: it simply defines the example problem.
 
 The Brusselator PDE is defined on a unit square periodic domain as follows:
@@ -208,7 +208,7 @@ information on linear solver choices, see the
 valid [LinearSolve.jl](https://linearsolve.sciml.ai/dev/) solver.
 
 !!! note
-
+    
     Switching to a Krylov linear solver will automatically change the ODE solver
     into Jacobian-free mode, dramatically reducing the memory required. This can
     be overridden by adding `concrete_jac=true` to the algorithm.

diff --git a/docs/src/tutorials/faster_ode_example.md b/docs/src/tutorials/faster_ode_example.md
@@ -1,7 +1,7 @@
 # [Code Optimization for Differential Equations](@id speed)
 
 !!! note
-
+    
     See [this FAQ](@ref faq_performance)
     for information on common pitfalls and how to improve performance.
 
@@ -95,7 +95,7 @@ When the in-place form is used, DifferentialEquations.jl takes a different
 internal route that minimizes the internal allocations as well.
 
 !!! note
-
+    
     Notice that nothing is returned. When in in-place form, the ODE solver ignores
     the return. Instead, make sure that the original `du` array is mutated instead
     of constructing a new array
@@ -370,7 +370,7 @@ In this tutorial, we will optimize the right-hand side definition of a PDE
 semi-discretization.
 
 !!! note
-
+    
     We highly recommend looking at the [Solving Large Stiff Equations](@ref stiff)
     tutorial for details on customizing DifferentialEquations.jl for more
     efficient large-scale stiff ODE solving. This section will only focus on the
@@ -690,7 +690,7 @@ algorithms. `CVODE_BDF` allows us to use a sparse Newton-Krylov solver by
 setting `linear_solver = :GMRES`.
 
 !!! note
-
+    
     The [Solving Large Stiff Equations](@ref stiff) tutorial goes through these
     details. This is simply to give a taste of how much optimization opportunity
     is left on the table!