docs: use @brownian in the stochastic_diffeq tutorial

Author: AayushSabharwal

docs/src/tutorials/stochastic_diffeq.md

@@ -5,7 +5,7 @@ to the model: randomness. This is a
 [stochastic differential equation](https://en.wikipedia.org/wiki/Stochastic_differential_equation)
 which has a deterministic (drift) component and a stochastic (diffusion)
 component. Let's take the Lorenz equation from the first tutorial and extend
-it to have multiplicative noise.
+it to have multiplicative noise by creating `@brownian` variables in the equations.
 
 ```@example SDE
 using ModelingToolkit, StochasticDiffEq
@@ -14,16 +14,12 @@ using ModelingToolkit: t_nounits as t, D_nounits as D
 # Define some variables
 @parameters σ ρ β
 @variables x(t) y(t) z(t)
+@brownian a
+eqs = [D(x) ~ σ * (y - x) + 0.1a * x,
+    D(y) ~ x * (ρ - z) - y + 0.1a * y,
+    D(z) ~ x * y - β * z + 0.1a * z]
 
-eqs = [D(x) ~ σ * (y - x),
-    D(y) ~ x * (ρ - z) - y,
-    D(z) ~ x * y - β * z]
-
-noiseeqs = [0.1 * x,
-    0.1 * y,
-    0.1 * z]
-
-@mtkbuild de = SDESystem(eqs, noiseeqs, t, [x, y, z], [σ, ρ, β])
+@mtkbuild de = System(eqs, t)
 
 u0map = [
     x => 1.0,
@@ -38,5 +34,5 @@ parammap = [
 ]
 
 prob = SDEProblem(de, u0map, (0.0, 100.0), parammap)
-sol = solve(prob, SOSRI())
+sol = solve(prob, LambdaEulerHeun())
 ```