DiffEqNoiseProcess.jl: Noise Processes for Stochastic Modeling

Noise processes are essential in continuous stochastic modeling. The NoiseProcess types are distributionally-exact, meaning they are not solutions of stochastic differential equations but instead are directly generated according to their analytical distributions. These processes are used as the noise term in the SDE and RODE solvers. Additionally, the noise processes themselves can be simulated and solved using the DiffEq common interface (including the Monte Carlo interface).

Installation

To install DiffEqNoiseProcess.jl, use the Julia package manager:

using Pkg
Pkg.add("DiffEqNoiseProcess")

Using Noise Processes

Passing a Noise Process to a Problem Type

AbstractNoiseProcesses can be passed directly to the problem types to replace the standard Wiener process (Brownian motion) with your choice of noise. To do this, simply construct the noise and pass it to the noise keyword argument:

using DiffEqNoiseProcess, SciMLBase
μ = 1.0
σ = 2.0
W = GeometricBrownianMotionProcess(μ, σ, 0.0, 1.0, 1.0)
# ...
# Define f,g,u0,tspan for a SDEProblem
# ...
# prob = SDEProblem(f, g, u0, tspan, noise = W)

t: 1-element Vector{Float64}:
 0.0
u: 1-element Vector{Float64}:
 1.0

Basic Interface

The NoiseProcess acts like a DiffEq solution. For some noise process W, you can get its ith timepoint like W[i] and the associated time W.t[i]. If the NoiseProcess has a bridging distribution defined, it can be interpolated to arbitrary time points using W(t). Note that every interpolated value is saved to the NoiseProcess so that way it can stay distributionally correct. A plot recipe is provided that plots the timeseries.

Direct Simulation of the Noise Process

Since the NoiseProcess types are distribution-exact and do not require the stochastic differential equation solvers, many times one would like to directly simulate trajectories from these processes. The NoiseProcess has a NoiseProblem type:

NoiseProblem(noise, tspan)

for which solve works. For example, we can simulate a distributionally-exact Geometric Brownian Motion solution by:

μ = 1.0
σ = 2.0
W = GeometricBrownianMotionProcess(μ, σ, 0.0, 1.0, 1.0)
prob = NoiseProblem(W, (0.0, 1.0))
sol = solve(prob; dt = 0.1)

t: 11-element Vector{Float64}:
 0.0
 0.1
 0.2
 0.30000000000000004
 0.4
 0.5
 0.6
 0.7
 0.7999999999999999
 0.8999999999999999
 0.9999999999999999
u: 11-element Vector{Float64}:
 1.0
 0.35780197326522667
 0.4348999339664691
 0.863095976540253
 0.721343883676095
 0.9918015328349641
 1.5322519972377155
 2.3633665747003687
 2.2858217549713307
 3.7992226530702067
 3.3100935963748013

solve requires that the dt is given, and that the solution it returns is a NoiseProcess which has stepped through the timespan. Because this follows the common interface, all of the normal functionality works. For example, we can use the Monte Carlo functionality as follows:

enprob = EnsembleProblem(prob)
sol = solve(enprob; dt = 0.1, trajectories = 100)

EnsembleSolution Solution of length 100 with uType:
NoiseProcess{Float64, 1, Float64, Float64, Float64, Vector{Float64}, DiffEqNoiseProcess.GeometricBrownianMotion{Float64, Float64}, DiffEqNoiseProcess.var"#34#35"{DiffEqNoiseProcess.GeometricBrownianMotion{Float64, Float64}}, Nothing, false, ResettableStacks.ResettableStack{Tuple{Float64, Float64, Float64}, false}, ResettableStacks.ResettableStack{Tuple{Float64, Float64, Float64}, false}, RSWM{Float64}, Nothing, RandomNumbers.Xorshifts.Xoroshiro128Plus}

simulates 100 Geometric Brownian Motions.

Direct Interface

Most of the time, a NoiseProcess is received from the solution of a stochastic or random differential equation, in which case sol.W gives the NoiseProcess and it is already defined along some timeseries. In other cases, NoiseProcess types are directly simulated (see below). However, NoiseProcess types can also be directly acted on. The basic functionality is given by calculate_step! to calculate a future time point, and accept_step! to accept the step. If steps are rejected, the Rejection Sampling with Memory algorithm is applied to keep the solution distributionally exact. This kind of stepping is done via:

W = WienerProcess(0.0, 1.0, 1.0)
dt = 0.1
W.dt = dt
u = nothing;
p = nothing; # for state-dependent distributions
calculate_step!(W, dt, u, p)
for i in 1:10
    accept_step!(W, dt, u, p)
end

Contributing

Please refer to the SciML ColPrac: Contributor's Guide on Collaborative Practices for Community Packages for guidance on PRs, issues, and other matters relating to contributing to SciML.
See the SciML Style Guide for common coding practices and other style decisions.
There are a few community forums:
- The #diffeq-bridged and #sciml-bridged channels in the Julia Slack
- The #diffeq-bridged and #sciml-bridged channels in the Julia Zulip
- On the Julia Discourse forums
- See also SciML Community page

Reproducibility

The documentation of this SciML package was built using these direct dependencies,

Status `~/work/DiffEqNoiseProcess.jl/DiffEqNoiseProcess.jl/docs/Project.toml`
  [77a26b50] DiffEqNoiseProcess v5.24.1 `~/work/DiffEqNoiseProcess.jl/DiffEqNoiseProcess.jl`
  [e30172f5] Documenter v1.10.2
  [0bca4576] SciMLBase v2.86.2

and using this machine and Julia version.

Julia Version 1.11.5
Commit 760b2e5b739 (2025-04-14 06:53 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 4 × AMD EPYC 7763 64-Core Processor
  WORD_SIZE: 64
  LLVM: libLLVM-16.0.6 (ORCJIT, znver3)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)

A more complete overview of all dependencies and their versions is also provided.

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