CorrelatedWienerProcess bridge function has not the correct arguments (Correlated Lorenz)

[oameye]

Describe the bug 🐞

When adding a CorrelatedWienerProcess to the lorenz ODE, the intergrator errors as it can not find the correct method for the bridge function made in the construction of the CorrelatedWienerProcess. The integrator gives 10 arguments, but the bridge function has 9.

Minimal Reproducible Example 👇

using StochasticDiffEq
using DiffEqNoiseProcess

Γ = [1.0 0.3 0.0; 0.3 1 0.5; 0.0 0.5 1.0]
W = CorrelatedWienerProcess(Γ,0.0,zeros(3),zeros(3))

tspan = (0.0, 1.0)
prob = NoiseProblem(W, (0.0, 1.0))
sol = solve(prob; dt = 0.01) # works

function lorenz_rule(u, p, t)
    σ = p[1]
    ρ = p[2]
    β = p[3]
    du1 = σ * (u[2] - u[1])
    du2 = u[1] * (ρ - u[3]) - u[2]
    du3 = u[1] * u[2] - β * u[3]
    return [du1, du2, du3]
end

u0 = [0, 10.0, 0]
g(u, p, t) = ones(3)
p0 = [10, 28, 8 / 3]

prob = SDEProblem(lorenz_rule, g, u0, tspan, p0; noise=W)
solve(prob, LambaEM())

Error & Stacktrace ⚠️

ERROR: MethodError: no method matching (::DiffEqNoiseProcess.var"#99#101")(::Vector{…}, ::NoiseProcess{…}, ::Int64, ::Vector{…}, ::Float64, ::Float64, ::Vector{…}, ::Vector{…}, ::Float64, ::RandomNumbers.Xorshifts.Xoroshiro128Plus)

Closest candidates are:
  (::DiffEqNoiseProcess.var"#99#101")(::Any, ::Any, ::Any, ::Any, ::Any, ::Any, ::Any, ::Any, ::Any)
   @ DiffEqNoiseProcess C:\Users\orjan\.julia\packages\DiffEqNoiseProcess\ezGyU\src\correlated_noisefunc.jl:11

Stacktrace:
  [1] reject_step!
    @ C:\Users\orjan\.julia\packages\DiffEqNoiseProcess\ezGyU\src\noise_interfaces\noise_process_interface.jl:292 [inlined]
  [2] reject_step! (repeats 2 times)
    @ C:\Users\orjan\.julia\packages\StochasticDiffEq\M3bKo\src\integrators\integrator_utils.jl:7 [inlined]
  [3] loopheader!
    @ C:\Users\orjan\.julia\packages\StochasticDiffEq\M3bKo\src\integrators\integrator_utils.jl:42 [inlined]
  [4] solve!(integrator::StochasticDiffEq.SDEIntegrator{…})
    @ StochasticDiffEq C:\Users\orjan\.julia\packages\StochasticDiffEq\M3bKo\src\solve.jl:614
  [5] __solve(prob::SDEProblem{…}, alg::LambaEM{…}, timeseries::Vector{…}, ts::Vector{…}, ks::Nothing, recompile::Type{…}; kwargs::@Kwargs{})
    @ StochasticDiffEq C:\Users\orjan\.julia\packages\StochasticDiffEq\M3bKo\src\solve.jl:7
  [6] __solve (repeats 5 times)
    @ C:\Users\orjan\.julia\packages\StochasticDiffEq\M3bKo\src\solve.jl:1 [inlined]
  [7] #solve_call#44
    @ C:\Users\orjan\.julia\packages\DiffEqBase\2SsGl\src\solve.jl:612 [inlined]
  [8] solve_call
    @ C:\Users\orjan\.julia\packages\DiffEqBase\2SsGl\src\solve.jl:569 [inlined]
  [9] #solve_up#53
    @ C:\Users\orjan\.julia\packages\DiffEqBase\2SsGl\src\solve.jl:1080 [inlined]
 [10] solve_up
    @ C:\Users\orjan\.julia\packages\DiffEqBase\2SsGl\src\solve.jl:1066 [inlined]
 [11] #solve#51
    @ C:\Users\orjan\.julia\packages\DiffEqBase\2SsGl\src\solve.jl:1003 [inlined]
 [12] solve(prob::SDEProblem{…}, args::LambaEM{…})
    @ DiffEqBase C:\Users\orjan\.julia\packages\DiffEqBase\2SsGl\src\solve.jl:993
 [13] top-level scope
    @ Untitled-1:28
Some type information was truncated. Use `show(err)` to see complete types.

Environment (please complete the following information):

• Output of using Pkg; Pkg.status()

  [77a26b50] DiffEqNoiseProcess v5.23.0
  [789caeaf] StochasticDiffEq v6.67.0

• Output of using Pkg; Pkg.status(; mode = PKGMODE_MANIFEST)

  [47edcb42] ADTypes v1.7.1
  [7d9f7c33] Accessors v0.1.37
  [79e6a3ab] Adapt v4.0.4
  [66dad0bd] AliasTables v1.1.3
  [ec485272] ArnoldiMethod v0.4.0
  [4fba245c] ArrayInterface v7.15.0
  [4c555306] ArrayLayouts v1.10.2
  [62783981] BitTwiddlingConvenienceFunctions v0.1.6
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  [2b5f629d] DiffEqBase v6.153.0
  [77a26b50] DiffEqNoiseProcess v5.23.0
  [163ba53b] DiffResults v1.1.0
  [b552c78f] DiffRules v1.15.1
  [a0c0ee7d] DifferentiationInterface v0.5.13
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⌅ [6b7a57c9] Expronicon v0.8.5
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  [856f044c] MKL_jll v2024.2.0+0
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⌅ [f50d1b31] Rmath_jll v0.4.3+0
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  [56f22d72] Artifacts
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  [8ba89e20] Distributed
  [f43a241f] Downloads v1.6.0
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  [9fa8497b] Future
  [b77e0a4c] InteractiveUtils
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  [44cfe95a] Pkg v1.10.0
  [de0858da] Printf
  [3fa0cd96] REPL
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  [ea8e919c] SHA v0.7.0
  [9e88b42a] Serialization
  [1a1011a3] SharedArrays
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• Output of versioninfo()

Julia Version 1.10.4
Commit 48d4fd4843 (2024-06-04 10:41 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Windows (x86_64-w64-mingw32)
  CPU: 12 × AMD Ryzen 5 5600X 6-Core Processor
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)
Threads: 10 default, 0 interactive, 5 GC (on 12 virtual cores)
Environment:
  JULIA_PKG_PRESERVE_TIERED_INSTALLED = true
  JULIA_EDITOR = code
  JULIA_NUM_THREADS = 10

[ChrisRackauckas]

CorrelatedWienerProcess does not have the bridge function defined, so it won't be compatible here. That's already tracked in SciML/DiffEqNoiseProcess.jl#85

[oameye]

Should we maybe add a warning or error when adaptive solver is used with correlated noise? Similar to #578.

[ChrisRackauckas]

Yes, it would be good to add traits to https://github.com/SciML/SciMLBase.jl/blob/master/src/alg_traits.jl for allows_non_wiener_noise which is false for any high order (that uses levy areas) or adaptive method, and then check for it.

[oameye]

Okay will make a PR this week

[rmsrosa]

Besides adding traits for allows_non_wiener_noise, I think the line
https://github.com/SciML/DiffEqNoiseProcess.jl/blob/master/src/correlated_noisefunc.jl#L11
should be fixed to have the correct number of arguments:

Current L11

    bridge = function (W, W0, Wh, q, h, u, p, t, rng)

should be

    bridge = function (dW, W, W0, Wh, q, h, u, p, t, rng)

[ChrisRackauckas]

Yes good point. Both would cause an error of course, but you'd get the nicer error message.