Advection-Diffusion¤
In 1D:
In higher dimensions:
(often just \(\vec{c} = c \vec{1}\)) and potentially with anisotropic diffusion.
exponax.stepper.AdvectionDiffusion
¤
Bases: BaseStepper
Source code in exponax/stepper/_advection_diffusion.py
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__init__
¤
__init__(
num_spatial_dims: int,
domain_extent: float,
num_points: int,
dt: float,
*,
velocity: Union[Float[Array, D], float] = 1.0,
diffusivity: Union[
Float[Array, "D D"], Float[Array, D], float
] = 0.01
)
Timestepper for the d-dimensional (d ∈ {1, 2, 3}
) advection-diffusion
equation on periodic boundary conditions.
In 1d, the advection-diffusion equation is given by
uₜ + c uₓ = ν uₓₓ
with c ∈ ℝ
being the velocity/advection speed and ν ∈ ℝ
being the
diffusivity.
In higher dimensions, the advection-diffusion equation can be written as
uₜ + c ⋅ ∇u = ν Δu
with c ∈ ℝᵈ
being the velocity/advection vector.
See also exponax.stepper.Diffusion
for additional details on
anisotropic diffusion.
Arguments:
num_spatial_dims
: The number of spatial dimensionsd
.domain_extent
: The size of the domainL
; in higher dimensions the domain is assumed to be a scaled hypercubeΩ = (0, L)ᵈ
.num_points
: The number of pointsN
used to discretize the domain. This includes the left boundary point and excludes the right boundary point. In higher dimensions; the number of points in each dimension is the same. Hence, the total number of degrees of freedom isNᵈ
.dt
: The timestep sizeΔt
between two consecutive states.velocity
(keyword-only): The advection speedc
. In higher dimensions, this can be a scalar (=float) or a vector of lengthd
. If a scalar is given, the advection speed is assumed to be the same in all spatial dimensions. Default:1.0
.diffusivity
(keyword-only): The diffusivityν
. In higher dimensions, this can be a scalar (=float), a vector of lengthd
, or a matrix of shaped ˣ d
. If a scalar is given, the diffusivity is assumed to be the same in all spatial dimensions. If a vector (of lengthd
) is given, the diffusivity varies across dimensions (=> diagonal diffusion). For a matrix, there is fully anisotropic diffusion. In this case,A
must be symmetric positive definite (SPD). Default:0.01
.
Notes:
- The stepper is unconditionally stable, no matter the choice of
any argument because the equation is solved analytically in Fourier
space. However, note that initial conditions with modes higher
than the Nyquist freuency (
(N//2)+1
withN
being thenum_points
) lead to spurious oscillations. - Ultimately, only the factors
c Δt / L
andν Δt / L²
affect the characteristic of the dynamics. See alsoexponax.stepper.generic.NormalizedLinearStepper
withnormalized_coefficients = [0, alpha_1, alpha_2]
withalpha_1 = - velocity * dt / domain_extent
andalpha_2 = diffusivity * dt / domain_extent**2
.
Source code in exponax/stepper/_advection_diffusion.py
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__call__
¤
__call__(
u: Float[Array, "C ... N"]
) -> Float[Array, "C ... N"]
Perform one step of the time integration for a single state.
Arguments:
u
: The state vector, shape(C, ..., N,)
.
Returns:
u_next
: The state vector after one step, shape(C, ..., N,)
.
Tip
Use this call method together with exponax.rollout
to efficiently
produce temporal trajectories.
Info
For batched operation, use jax.vmap
on this function.
Source code in exponax/_base_stepper.py
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