Christopher Rackauckas is a bit of a pied piper with his Julia work. What appeals to me is that the DSL looks pretty slick for certain classes of problems. From https://sciml.ai/roadmap/ this model of a chemical reaction network, which is the way I think about the problem domain.

rs = @reaction_network begin
c1, S + E --> SE
c2, SE --> S + E
c3, SE --> P + E
end c1 c2 c3
p = (0.00166,0.0001,0.1)
tspan = (0., 100.)
u0 = [301., 100., 0., 0.] # S = 301, E = 100, SE = 0, P = 0

# solve ODEs
oprob = ODEProblem(rs, u0, tspan, p)
osol = solve(oprob, Tsit5())

# solve JumpProblem
u0 = [301, 100, 0, 0]
dprob = DiscreteProblem(rs, u0, tspan, p)
jprob = JumpProblem(dprob, Direct(), rs)
jsol = solve(jprob, SSAStepper())


Or this statement from https://tobydriscoll.net/blog/matlab-vs.-julia-vs.-python/

> If you believe that V.conj().T@D**3@V is an elegant way to write \\(V^*D^3V\\) , then you may need to see a doctor.