# SDE Problems

## Mathematical Specification of a SDE Problem

To define an SDE Problem, you simply need to give the forcing function f, the noise function g, and the initial condition u₀ which define an SDE:

$$$du = f(u,p,t)dt + Σgᵢ(u,p,t)dWⁱ$$$

f and g should be specified as f(u,p,t) and g(u,p,t) respectively, and u₀ should be an AbstractArray whose geometry matches the desired geometry of u. Note that we are not limited to numbers or vectors for u₀; one is allowed to provide u₀ as arbitrary matrices / higher dimension tensors as well. A vector of gs can also be defined to determine an SDE of higher Ito dimension.

## Problem Type

Wraps the data which defines an SDE problem

$$$u = f(u,p,t)dt + Σgᵢ(u,p,t)dWⁱ$$$

with initial condition u0.

### Constructors

• SDEProblem(f::SDEFunction,g,u0,tspan,p=NullParameters();noise=WHITE_NOISE,noise_rate_prototype=nothing)
• SDEProblem{isinplace}(f,g,u0,tspan,p=NullParameters();noise=WHITE_NOISE,noise_rate_prototype=nothing) : Defines the SDE with the specified functions. The default noise is WHITE_NOISE. isinplace optionally sets whether the function is inplace or not. This is determined automatically, but not inferred.

Parameters are optional, and if not given then a NullParameters() singleton will be used which will throw nice errors if you try to index non-existent parameters. Any extra keyword arguments are passed on to the solvers. For example, if you set a callback in the problem, then that callback will be added in every solve call.

For specifying Jacobians and mass matrices, see the DiffEqFunctions page.

### Fields

• f: The drift function in the SDE.
• g: The noise function in the SDE.
• u0: The initial condition.
• tspan: The timespan for the problem.
• p: The optional parameters for the problem. Defaults to NullParameters.
• noise: The noise process applied to the noise upon generation. Defaults to Gaussian white noise. For information on defining different noise processes, see the noise process documentation page
• noise_rate_prototype: A prototype type instance for the noise rates, that is the output g. It can be any type which overloads A_mul_B! with itself being the middle argument. Commonly, this is a matrix or sparse matrix. If this is not given, it defaults to nothing, which means the problem should be interpreted as having diagonal noise.
• kwargs: The keyword arguments passed onto the solves.

## Example Problems

Examples problems can be found in DiffEqProblemLibrary.jl.

To use a sample problem, such as prob_sde_linear, you can do something like:

#] add DiffEqProblemLibrary
using DiffEqProblemLibrary.SDEProblemLibrary
SDEProblemLibrary.importsdeproblems()
prob = SDEProblemLibrary.prob_sde_linear
sol = solve(prob)
DiffEqProblemLibrary.SDEProblemLibrary.prob_sde_2DlinearConstant

8 linear SDEs (as a 4x2 matrix):

$$$du_t = αudt + βudW_t$$$

where $α=1.01$, $β=0.87$, and initial condtion $u_0=\frac{1}{2}$ with solution

$$$u(u_0,p,t,W_t)=u_0\exp((α-\frac{β^2}{2})t+βW_t)$$$
DiffEqProblemLibrary.SDEProblemLibrary.prob_sde_waveConstant
$$$du_t = -\frac{1}{100}\sin(u)\cos^3(u)dt + \frac{1}{10}\cos^{2}(u_t) dW_t$$$

and initial condition $u_0=1$ with solution

$$$u(u_0,p,t,W_t)=\arctan(\frac{W_t}{10} + \tan(u_0))$$$
DiffEqProblemLibrary.SDEProblemLibrary.prob_sde_lorenzConstant

$$$dx = σ(y-x)dt + αdW_t$$$$$$dy = (x(ρ-z) - y)dt + αdW_t$$$$$$dz = (xy - βz)dt + αdW_t$$$

with $σ=10$, $ρ=28$, $β=8/3$, $α=3.0$ and inital condition $u_0=[1;1;1]$.

DiffEqProblemLibrary.SDEProblemLibrary.prob_sde_cubicConstant
$$$du_t = \frac{1}{4}u(1-u^2)dt + \frac{1}{2}(1-u^2)dW_t$$$

and initial condtion $u_0=\frac{1}{2}$, with solution

$$$u(u0,p,t,W_t)=\frac{(1+u_0)\exp(W_t)+u)0-1}{(1+u_0)\exp(W_t)+1-u_0}$$$
DiffEqProblemLibrary.SDEProblemLibrary.prob_sde_additiveConstant

$$$u_t = (\frac{β}{\sqrt{1+t}}-\frac{1}{2(1+t)}u_t)dt + \frac{αβ}{\sqrt{1+t}}dW_t$$$

and initial condition $u_0=1$ with $α=0.1$ and $β=0.05$, with solution

$$$u(u_0,p,t,W_t)=\frac{u_0}{\sqrt{1+t}} + \frac{β(t+αW_t)}{\sqrt{1+t}}$$$
DiffEqProblemLibrary.SDEProblemLibrary.oval2ModelExampleFunction

oval2ModelExample(;largeFluctuations=false,useBigs=false,noiseLevel=1)

A function which generates the Oval2 Epithelial-Mesenchymal Transition model from:

Rackauckas, C., & Nie, Q. (2017). Adaptive methods for stochastic differential equations via natural embeddings and rejection sampling with memory. Discrete and continuous dynamical systems. Series B, 22(7), 2731.

19 SDEs which are only stiff during transitions between biological states.

DiffEqProblemLibrary.SDEProblemLibrary.prob_sde_stiffquadstratConstant

The composite Euler method for stiff stochastic differential equations

Kevin Burrage, Tianhai Tian

And

S-ROCK: CHEBYSHEV METHODS FOR STIFF STOCHASTIC DIFFERENTIAL EQUATIONS

ASSYR ABDULLE AND STEPHANE CIRILLI

Stiffness of Euler is determined by α+β²<1 Higher α or β is stiff, with α being deterministic stiffness and β being noise stiffness (and grows by square).

DiffEqProblemLibrary.SDEProblemLibrary.prob_sde_stiffquaditoConstant

The composite Euler method for stiff stochastic differential equations

Kevin Burrage, Tianhai Tian

And

S-ROCK: CHEBYSHEV METHODS FOR STIFF STOCHASTIC DIFFERENTIAL EQUATIONS

ASSYR ABDULLE AND STEPHANE CIRILLI

Stiffness of Euler is determined by α+β²<1 Higher α or β is stiff, with α being deterministic stiffness and β being noise stiffness (and grows by square).