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Automatic inference of discrete parameters in events #3991

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Automatic inference of discrete parameters in events #3991

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f944212
infer `discrete_parameters` in all events
TorkelE Oct 26, 2025
074cde5
add tests
TorkelE Oct 26, 2025
1afe8ca
more tests
TorkelE Oct 26, 2025
b140423
fix previous erroneous test
TorkelE Oct 26, 2025
5dc07f2
formatting
TorkelE Oct 26, 2025
aa40b5c
CI fix
TorkelE Oct 26, 2025
96935e0
Merge remote-tracking branch 'origin/master' into event_discpars_auto…
TorkelE Nov 15, 2025
4f83056
up
TorkelE Nov 15, 2025
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32 changes: 30 additions & 2 deletions test/symbolic_events.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -1374,7 +1374,8 @@ end
@parameters p2(t) = 1.0
@variables x(t) = 0.0
@variables x2(t)
event = [0.5] => [p2 ~ Pre(t)]
event = ModelingToolkit.SymbolicDiscreteCallback(
[0.5] => [p2 ~ Pre(t)]; discrete_parameters = [p2])

eq = [
D(x) ~ p2,
Expand All @@ -1385,7 +1386,7 @@ end
prob = ODEProblem(sys, [], (0.0, 1.0))
sol = solve(prob)
@test SciMLBase.successful_retcode(sol)
@test sol[x, end]≈1.0 atol=1e-6
@test sol[x, end]≈0.75 atol=1e-6
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@TorkelE TorkelE Oct 26, 2025

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There is an error in this test. The discrete_parameters argument was forgotten, so the event is not actually triggered. Unless the intention of the test is to check that everything works as (un)expected when the input is omitted (but the naming and test do not suggest that), this should be changed. With this PR, the event is now triggered, and the correct value 0.75 is achieved.

end

@testset "Symbolic affects are compiled in `complete`" begin
Expand Down Expand Up @@ -1535,3 +1536,30 @@ end
@test v_sol[v] ≈ [vini, -vini, vini]
@test M_sol[M] ≈ [Mini, -Mini, Mini]
end

@testset "Check array parameter and variables event" begin
# Creates the model using the macro.
us = @variables begin
X(t)[1:2] = [4.0, 4.0]
end
ps = @parameters begin
k(t)[1:2] = [1, 1]
kup = 2.0
end
eqs = [
D(X[1]) ~ -k[1]*X[1] + k[2]*X[2]
D(X[2]) ~ k[1]*X[1] - k[2]*X[2]
]
c_event = SymbolicContinuousCallback(
(k[2] ~ t) => [k[1] ~ Pre(k[1] + kup)]; discrete_parameters = [k[1]])
@mtkcompile model = System(eqs, t, us, ps; continuous_events = [c_event])

# Simulates the model. Checks that the correct values are achieved.
prob = ODEProblem(model, [], (0.0, 100.0))
sol = solve(prob, Rosenbrock23())
@test sol.ps[model.kup] == 2.0
@test sol.ps[model.k[1]][end] == 3.0
@test sol.ps[model.k[2]][end] == 1.0
@test sol[model.X[1]][end]≈2.0 atol=1e-8 rtol=1e-8
@test sol[model.X[2]][end]≈6.0 atol=1e-8 rtol=1e-8
end
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