EnsembleProblem with NonlinearProblem

[oameye]

Describe the bug 🐞

Solving a NonlinearProblem with an EnsembleProblem stopped working

Minimal Reproducible Example 👇

using NonlinearSolve, NonlinearSolveHomotopyContinuation

Δsweep = range(-0.01, 0.03, 101)
Kval = 0.01
Fval = 0.002
κval = 0.001

function mean_field(du, u, p)
    F, Δ, K, κ, n, m_r, m_i = p
    u1, v1 = u
    du[1] = -F - (2*K*m_r + 4*K*n + 2*K*u1^2 + 2*K*v1^2 - Δ) * u1 - (2*K*m_i + κ / 2) * v1
    du[2] = (2*K*m_i - κ / 2) * u1 + (4*K*n + 2*K*u1^2 + 2*K*v1^2 - Δ - 2*K*m_r) * v1
    return nothing
end
function jacobian(du, u, p)
    F, Δ, K, κ, n, m_r, m_i = p
    u1, v1 = u
    du[1, 1] = -0.5*κ + 2*K*m_i + 4*K*u1*v1
    du[2,2] = -0.5*κ - 2*K*m_i - 4*K*u1*v1
    du[1,2] = - Δ -2*K*m_r + 4*K*n + 2*K*u1^2 + 6*K*v1^2
    du[2,1] = Δ - 2*K*m_r - 4*K*n - 6*K*u1^2 - 2*K*v1^2
    return nothing
end
# Example parameters with n=0, m_r=0, m_i=0
p = (; F=Fval, Δ=0.01, K=Kval, κ=κval, n=0, m_r=0, m_i=0)  
u0 = [0.0, 0.0]
fn = NonlinearFunction(fn; jac=jacobian)
prob = NonlinearProblem{true}(mean_field, u0, p)
sol = solve(prob) # works

alg = HomotopyContinuationJL{true}()
sol = solve(prob, alg) # works

function prob_func(prob, i, repeat)
    p = (;F = 0.002, Δ = Δsweep[i], K = 0.01, κ = 0.001, n = 0, m_r = 0, m_i = 0)
    MTK.remake(prob; p)
end
ensemble = EnsembleProblem(prob; prob_func)
sol_ensemble = solve(ensemble, alg)

Error & Stacktrace ⚠️

MethodError: no method matching __solve(::EnsembleProblem{NonlinearProblem{Vector{Float64}, true, @NamedTuple{F::Float64, Δ::Float64, K::Float64, κ::Float64, n::Int64, m_r::Int64, m_i::Int64}, NonlinearFunction{true, SciMLBase.FullSpecialize, typeof(mean_field), UniformScaling{Bool}, Nothing, Nothing, typeof(jacobian), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}, SciMLBase.StandardNonlinearProblem}, typeof(prob_func), typeof(SciMLBase.DEFAULT_OUTPUT_FUNC), typeof(SciMLBase.DEFAULT_REDUCTION), Nothing}, ::HomotopyContinuationJL{true, AutoForwardDiff{nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}})
The function `__solve` exists, but no method is defined for this combination of argument types.

Closest candidates are:
  __solve(::SciMLBase.AbstractEnsembleProblem, ::A, !Matched::SciMLBase.BasicEnsembleAlgorithm; trajectories, batch_size, progress_aggregate, pmap_batch_size, kwargs...) where A
   @ SciMLBase ~/.julia/packages/SciMLBase/LFeTa/src/ensemble/basic_ensemble_solve.jl:164
  __solve(!Matched::SciMLBase.AbstractNonlinearProblem, ::NonlinearSolveBase.AbstractNonlinearSolveAlgorithm, Any...; kwargs...)
   @ NonlinearSolveBase ~/.julia/packages/NonlinearSolveBase/2E600/src/solve.jl:223
  __solve(!Matched::SciMLBase.AbstractNonlinearProblem, ::Any...; default_set, second_time, kwargs...)
   @ NonlinearSolveBase ~/.julia/packages/NonlinearSolveBase/2E600/src/solve.jl:352
  ...


Stacktrace:
 [1] solve(prob::EnsembleProblem{NonlinearProblem{Vector{Float64}, true, @NamedTuple{F::Float64, Δ::Float64, K::Float64, κ::Float64, n::Int64, m_r::Int64, m_i::Int64}, NonlinearFunction{true, SciMLBase.FullSpecialize, typeof(mean_field), UniformScaling{Bool}, Nothing, Nothing, typeof(jacobian), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing}, @Kwargs{}, SciMLBase.StandardNonlinearProblem}, typeof(prob_func), typeof(SciMLBase.DEFAULT_OUTPUT_FUNC), typeof(SciMLBase.DEFAULT_REDUCTION), Nothing}, args::HomotopyContinuationJL{true, AutoForwardDiff{nothing, Nothing}, @Kwargs{}}; kwargs::@Kwargs{})
   @ SciMLBase ~/.julia/packages/SciMLBase/LFeTa/src/ensemble/basic_ensemble_solve.jl:361
 [2] solve(prob::EnsembleProblem{NonlinearProblem{Vector{Float64}, true, @NamedTuple{F::Float64, Δ::Float64, K::Float64, κ::Float64, n::Int64, m_r::Int64, m_i::Int64}, NonlinearFunction{true, SciMLBase.FullSpecialize, typeof(mean_field), UniformScaling{Bool}, Nothing, Nothing, typeof(jacobian), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing}, @Kwargs{}, SciMLBase.StandardNonlinearProblem}, typeof(prob_func), typeof(SciMLBase.DEFAULT_OUTPUT_FUNC), typeof(SciMLBase.DEFAULT_REDUCTION), Nothing}, args::HomotopyContinuationJL{true, AutoForwardDiff{nothing, Nothing}, @Kwargs{}})
   @ SciMLBase ~/.julia/packages/SciMLBase/LFeTa/src/ensemble/basic_ensemble_solve.jl:356
 [3] top-level scope

Environment (please complete the following information):

• Output of using Pkg; Pkg.status()

  [961ee093] ModelingToolkit v10.26.0
  [8913a72c] NonlinearSolve v4.12.0
  [2ac3b008] NonlinearSolveHomotopyContinuation v0.1.7
  [1dea7af3] OrdinaryDiffEq v6.102.1
⌅ [d1185830] SymbolicUtils v3.32.0
  [0c5d862f] Symbolics v6.56.0
  [8ea1fca8] TermInterface v2.0.0
  [37e2e46d] LinearAlgebra v1.11.0

• Output of versioninfo()

Julia Version 1.11.7
Commit f2b3dbda30a (2025-09-08 12:10 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 12 × AMD Ryzen 5 5600X 6-Core Processor
  WORD_SIZE: 64
  LLVM: libLLVM-16.0.6 (ORCJIT, znver3)
Threads: 10 default, 0 interactive, 5 GC (on 12 virtual cores)
Environment:
  JULIA_EDITOR = code
  JULIA_VSCODE_REPL = 1
  JULIA_NUM_THREADS = 10

[oameye]

I am sorry, was to fast:

This works

function prob_func(prob, i, repeat)
    p = (;F = 0.002, Δ = Δsweep[i], K = 0.01, κ = 0.001, n = 0, m_r = 0, m_i = 0)
    MTK.remake(prob; p)
end
ensemble = EnsembleProblem(prob; prob_func)
sol_ensemble = solve(ensemble, alg, EnsembleSerial(); trajectories=length(Δsweep))