Gradient NormA¤
exponax.normalized.NormalizedGradientNormStepper
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Bases: BaseStepper
Source code in exponax/normalized/_gradient_norm.py
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__init__
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__init__(
num_spatial_dims: int,
num_points: int,
*,
normalized_coefficients: tuple[float, ...] = (
0.0,
0.0,
-1.0 * 0.1 / 60.0**2,
0.0,
-1.0 * 0.1 / 60.0**4,
),
normalized_gradient_norm_scale: float = 1.0
* 0.1
/ 60.0**2,
order: int = 2,
dealiasing_fraction: float = 2 / 3,
num_circle_points: int = 16,
circle_radius: float = 1.0
)
the number of channels do not grow with the number of spatial dimensions. They are always 1.
By default: the KS equation on L=60.0
Source code in exponax/normalized/_gradient_norm.py
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__call__
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__call__(
u: Float[Array, "C ... N"]
) -> Float[Array, "C ... N"]
Performs a check
Source code in exponax/_base_stepper.py
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