Comments (2)
I wouldn't consider this a bug, it's not expected to work. To make this work, an explicit overload would be required that breaks the problem up into N individual problems and then recombine the result in the end.
from modelingtoolkit.jl.
This is a temporary workaround
using ModelingToolkit
using MonteCarloMeasurements
pars = @parameters a, b
vars = @variables x, y
eqs = [
x ~ a
y ~ x + b
]
@mtkbuild model = NonlinearSystem(eqs, vars, pars) #split=false
u0 = Dict(
x => 1,
y => 2
)
function solve_wrapper(a0, b0)
p = Dict(
model.a => a0,
model.b => b0
)
prob = NonlinearProblem(model, u0, p)
sol = solve(prob)
[sol[model.x], sol[model.y]]
end
a0 = 1
b0 = 1
solve_wrapper(a0, b0)
a = 1 ± 0.1
b = 1 ± 0.1
xy = @bymap solve_wrapper(a, b)
julia> xy = @bymap solve_wrapper(a, b)
2-element Vector{Particles{Float64, 2000}}:
1.0 ± 0.1
2.0 ± 0.14
from modelingtoolkit.jl.
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from modelingtoolkit.jl.