Particle propagator

PushRandomDiffusion

Advances var.

What it solves

For each marker

p

, solves

d x_{p} (t) = 2 D d B_{t},

where $D > 0$ is a positive diffusion coefficient and $d B_{t}$ is a Wiener process,

B_{t + Δ t} - B_{t} = Δ t N (0; 1),

with $N (0; 1)$ denoting the standard normal distribution with mean zero and variance one.

Available algorithms:

* forward_euler (1st order)

The variables this step updates over a time step dt, and the discrete spaces each one accepts.

Name	Kind	Accepted spaces
`var`	PICVariable	`Particles3D`

Set these on the propagator in a parameter file, for example PushRandomDiffusion.Options(butcher=...).

Option	Default	Choices	Description
`butcher`	`None`	ButcherTableau	Butcher tableau used for explicit integration. If `None`, defaults to `ButcherTableau()`.
`bc_type`	`('periodic', 'periodic', 'periodic')`	tuple	Boundary-condition types per logical coordinate.
`diff_coeff`	`1`	float	Positive diffusion coefficient $D$ used in the stochastic increment.

Each model splits its time step into a sequence of propagators. This one appears in 1 model, at the position shown.