scimba_torch.geometry.regularized_sdf_projectors¶

A module for learning regularized signed distance functions.

Functions

learn_regularized_sdf([points_file, ...])

Learn a SDF from either a file of points or a parametric hypersurface.

Classes

`RegularizedSdfAnagram`([points_file, ...])	The class for Regularized SDF projectors with Anagram.
`RegularizedSdfEnergyNaturalGradient`([...])	The class for Regularized SDF projectors with Energy Natural Gradient.
`RegularizedSdfPinnsElliptic`([points_file, ...])	The class for Regularized SDF projectors without preconditioning.
`RegularizedSdfProjector`([points_file, ...])	The abstract class for Regularized SDF projectors.

class RegularizedSdfProjector(points_file=None, parametric_hyper_surface=None, bounding_domain=None, **kwargs)[source]¶

Bases: ABC

The abstract class for Regularized SDF projectors.

SDF = Signed Distance Function

Parameters:

points_file (str | None) – A .txt file of points on the curve, default to None.
parametric_hyper_surface (ParametricHyperSurface | None) – a parametric HyperSurface, default to None. One among points_file, parametric_hyper_surface must be provided.
bounding_domain (VolumetricDomain | list[tuple[float, float]] | Tensor | None) – a bounding domain for the surface. Mandatory if parametric_hyper_surface is given.
**kwargs – arbitrary keyword arguments

Keyword Arguments:

architecture – the architecture of the NN to be used (default: GenericMLP).
layer_sizes – the size of the hidden layers (default: [10] * 4).
activation_type – the activation function (default: “sine”).
...

class RegularizedSdfPinnsElliptic(points_file=None, parametric_hyper_surface=None, bounding_domain=None, **kwargs)[source]¶

Bases: RegularizedSdfProjector, PinnsElliptic

The class for Regularized SDF projectors without preconditioning.

SDF = Signed Distance Function

Parameters:

points_file (str | None) – A .txt file of points on the curve, default to None.
parametric_hyper_surface (ParametricHyperSurface | None) – a parametric HyperSurface, default to None. One among points_file, parametric_hyper_surface must be provided.
bounding_domain (VolumetricDomain | list[tuple[float, float]] | Tensor | None) – a bounding domain for the surface. Mandatory if parametric_hyper_surface is given.
**kwargs – arbitrary keyword arguments

Keyword Arguments:

architecture – the architecture of the NN to be used (default: GenericMLP).
layer_sizes – the size of the hidden layers (default: [10] * 4).
activation_type – the activation function (default: “sine”).
...

class RegularizedSdfEnergyNaturalGradient(points_file=None, parametric_hyper_surface=None, bounding_domain=None, **kwargs)[source]¶

Bases: RegularizedSdfProjector, NaturalGradientPinnsElliptic

The class for Regularized SDF projectors with Energy Natural Gradient.

SDF = Signed Distance Function

Parameters:

points_file (str | None) – A .txt file of points on the curve, default to None.
parametric_hyper_surface (ParametricHyperSurface | None) – a parametric HyperSurface, default to None. One among points_file, parametric_hyper_surface must be provided.
bounding_domain (VolumetricDomain | list[tuple[float, float]] | Tensor | None) – a bounding domain for the surface. Mandatory if parametric_hyper_surface is given.
**kwargs – arbitrary keyword arguments

Keyword Arguments:

architecture – the architecture of the NN to be used (default: GenericMLP).
layer_sizes – the size of the hidden layers (default: [10] * 4).
activation_type – the activation function (default: “sine”).
...

class RegularizedSdfAnagram(points_file=None, parametric_hyper_surface=None, bounding_domain=None, **kwargs)[source]¶

Bases: RegularizedSdfProjector, AnagramPinnsElliptic

The class for Regularized SDF projectors with Anagram.

SDF = Signed Distance Function

Parameters:

points_file (str | None) – A .txt file of points on the curve, default to None.
parametric_hyper_surface (ParametricHyperSurface | None) – a parametric HyperSurface, default to None. One among points_file, parametric_hyper_surface must be provided.
bounding_domain (VolumetricDomain | list[tuple[float, float]] | Tensor | None) – a bounding domain for the surface. Mandatory if parametric_hyper_surface is given.
**kwargs – arbitrary keyword arguments

Keyword Arguments:

architecture – the architecture of the NN to be used (default: GenericMLP).
layer_sizes – the size of the hidden layers (default: [10] * 4).
activation_type – the activation function (default: “sine”).
...

learn_regularized_sdf(points_file=None, parametric_hyper_surface=None, bounding_domain=None, mode='new', load_from=None, save_to=None, **kwargs)[source]¶

Learn a SDF from either a file of points or a parametric hypersurface.

SDF = Signed Distance Function

Parameters:

points_file (str | None) – A .txt file of points on the curve, default to None.
parametric_hyper_surface (ParametricHyperSurface | None) – a parametric HyperSurface, default to None. One among points_file, parametric_hyper_surface must be provided.
bounding_domain (VolumetricDomain | list[tuple[float, float]] | Tensor | None) – a bounding domain for the surface. Mandatory if parametric_hyper_surface is given.
mode (str) – either “new” for new solving, “load” for loading from a file or “resume” for loading from a file and continue solving.
load_from (str | None) – the file from which loading the model.
save_to (str | None) – the file where saving the model.
**kwargs – arbitrary keyword arguments

Keyword Arguments:

architecture – the architecture of the NN to be used (default: GenericMLP).
layer_sizes – the size of the hidden layers (default: [10] * 4).
activation_type – the activation function (default: “sine”).
epochs – the number of optimization steps
n_collocation – the number of collocation points in the domain
n_bc_collocation – the number of collocation points on the contour
...

Return type:

PinnsElliptic

Returns:

the PINN approximating the SDF

Raises:

NotImplementedError – the preconditioner is not known
ValueError – when loading file is not provided