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SciML · May 20, 2024 · 0b42acc · 0b42acc
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diff --git a/docs/src/examples/ode/second_order_adjoints.md b/docs/src/examples/ode/second_order_adjoints.md
@@ -33,7 +33,7 @@ ode_data = Array(solve(prob_trueode, Tsit5(), saveat = tsteps))
 
 dudt2 = Chain(x -> x .^ 3, Dense(2, 50, tanh), Dense(50, 2))
 ps, st = Lux.setup(Random.default_rng(), dudt2)
-function neuralodefunc(u,p,t)
+function neuralodefunc(u, p, t)
     dudt2(u, p, st)[1]
 end
 function prob_neuralode(u0, p)

diff --git a/docs/src/manual/differential_equation_sensitivities.md b/docs/src/manual/differential_equation_sensitivities.md
@@ -73,7 +73,7 @@ differentiation). Generally:
 
   - Continuous sensitivity analysis methods only support a subset of
     equations, which currently includes:
-
+    
       + ODEProblem (with mass matrices for differential-algebraic equations (DAEs)
       + SDEProblem
       + SteadyStateProblem / NonlinearProblem
@@ -110,7 +110,7 @@ is:
     `TrackerAdjoint` with an out-of-place definition may currently be the best option.
 
 !!! note
-
+    
     Compatibility with direct automatic differentiation algorithms (`ForwardDiffSensitivity`,
     `ReverseDiffAdjoint`, etc.) can be queried using the
     `SciMLBase.isautodifferentiable(::SciMLAlgorithm)` trait function.