Koopman() now supports infinite integration domains

[djinnome]

According to the SciML tutorial,

https://github.com/SciML/SciMLTutorials.jl/blob/e8c8c3e51703fd08b874abbbc7b52ed895d0cb6f/tutorials/DiffEqUncertainty/01-expectation_introduction.jmd#L111

However, when I actually tried this, I got the right answer:

u0_dist = [Normal(3.0,2.0)]
expectation(g, prob, u0_dist, p, Koopman(), Tsit5())

u: 1-element Vector{Float64}:
 0.9035426476099575

Compared with the analytical solution:

exp(p[1]*4.0)*mean(u0_dist[1])

0.9035826357366064

[djinnome]

I am running Julia 1.8 with Plots v1.31.7, DifferentialEquations v7.2.0 and SciMLBase v1.51.1

[ChrisRackauckas]

yeah it's getting a lot of work. Good to hear this is now fixed. Moving this to SciMLExpectations where it will be doctested as https://scimlexpectations.sciml.ai/dev/tutorials/introduction/