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anypinn.catalog.poisson_2d

U_KEY = 'u' module-attribute

Poisson2DDataModule

Bases: PINNDataModule

DataModule for 2D Poisson equation on [0,1]^2.

Generates a meshgrid with the analytic solution u(x,y) = sin(pix)sin(pi*y). The data is used for prediction/validation only -- training uses PDEResidualConstraint + DirichletBCConstraints (no DataConstraint).

Source code in src/anypinn/catalog/poisson_2d.py 
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class Poisson2DDataModule(PINNDataModule):
    """DataModule for 2D Poisson equation on [0,1]^2.

    Generates a meshgrid with the analytic solution u(x,y) = sin(pi*x)*sin(pi*y).
    The data is used for prediction/validation only -- training uses
    PDEResidualConstraint + DirichletBCConstraints (no DataConstraint).
    """

    def __init__(
        self,
        hp: PINNHyperparameters,
        grid_size: int = 30,
        validation: ValidationRegistry | None = None,
        callbacks: list[DataCallback] | None = None,
    ):
        self.grid_size = grid_size
        super().__init__(hp, validation, callbacks)

    @override
    def gen_data(self, config: GenerationConfig) -> tuple[Tensor, Tensor]:
        """Generate analytic solution on a 2D meshgrid for prediction."""
        xs = torch.linspace(0, 1, self.grid_size)
        ys = torch.linspace(0, 1, self.grid_size)
        grid_x, grid_y = torch.meshgrid(xs, ys, indexing="ij")

        x_grid = torch.stack([grid_x.reshape(-1), grid_y.reshape(-1)], dim=1)  # (N, 2)

        u_analytic = torch.sin(math.pi * x_grid[:, 0]) * torch.sin(math.pi * x_grid[:, 1])

        # Shape: (N, 1, 1) to match codebase convention
        y_data = u_analytic.unsqueeze(-1).unsqueeze(1)

        return x_grid, y_data

grid_size = grid_size instance-attribute

__init__(hp: PINNHyperparameters, grid_size: int = 30, validation: ValidationRegistry | None = None, callbacks: list[DataCallback] | None = None)

Source code in src/anypinn/catalog/poisson_2d.py 
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def __init__(
    self,
    hp: PINNHyperparameters,
    grid_size: int = 30,
    validation: ValidationRegistry | None = None,
    callbacks: list[DataCallback] | None = None,
):
    self.grid_size = grid_size
    super().__init__(hp, validation, callbacks)

gen_data(config: GenerationConfig) -> tuple[Tensor, Tensor]

Generate analytic solution on a 2D meshgrid for prediction.

Source code in src/anypinn/catalog/poisson_2d.py 
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@override
def gen_data(self, config: GenerationConfig) -> tuple[Tensor, Tensor]:
    """Generate analytic solution on a 2D meshgrid for prediction."""
    xs = torch.linspace(0, 1, self.grid_size)
    ys = torch.linspace(0, 1, self.grid_size)
    grid_x, grid_y = torch.meshgrid(xs, ys, indexing="ij")

    x_grid = torch.stack([grid_x.reshape(-1), grid_y.reshape(-1)], dim=1)  # (N, 2)

    u_analytic = torch.sin(math.pi * x_grid[:, 0]) * torch.sin(math.pi * x_grid[:, 1])

    # Shape: (N, 1, 1) to match codebase convention
    y_data = u_analytic.unsqueeze(-1).unsqueeze(1)

    return x_grid, y_data