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poissson2d Inverse

Loss Function Implementation for 2D Poisson Inverse Problem.

This module implements the loss function for solving the inverse Poisson equation with constant coefficient using neural networks. It focuses on computing residuals in the weak form of the PDE for diffusion coefficient identification.

Key functions
  • pde_loss_poisson_inverse: Computes domain-based PDE loss for constant coefficient identification
Note

The implementation is based on the FastVPINNs methodology [1] for efficient computation of Variational residuals of PDEs.

References

[1] FastVPINNs: Tensor-Driven Acceleration of VPINNs for Complex Geometries DOI: https://arxiv.org/abs/2404.12063

pde_loss_poisson_inverse(test_shape_val_mat, test_grad_x_mat, test_grad_y_mat, pred_nn, pred_grad_x_nn, pred_grad_y_nn, forcing_function, bilinear_params, inverse_params_dict)

Calculates residual for Poisson inverse problem with constant coefficient.

Implements the FastVPINNs methodology for computing variational residuals in 2D Poisson inverse problems (-∇·(ε∇u) = f) with unknown constant diffusion coefficient.

Parameters:

Name Type Description Default
test_shape_val_mat Tensor

Test function values at quadrature points Shape: (n_elements, n_test_functions, n_quad_points)

 required
test_grad_x_mat Tensor

Test function x-derivatives at quadrature points Shape: (n_elements, n_test_functions, n_quad_points)

 required
test_grad_y_mat Tensor

Test function y-derivatives at quadrature points Shape: (n_elements, n_test_functions, n_quad_points)

 required
pred_nn Tensor

Neural network solution at quadrature points Shape: (n_elements, n_quad_points)

 required
pred_grad_x_nn Tensor

x-derivative of NN solution at quadrature points Shape: (n_elements, n_quad_points)

 required
pred_grad_y_nn Tensor

y-derivative of NN solution at quadrature points Shape: (n_elements, n_quad_points)

 required
forcing_function callable

Right-hand side forcing term

 required
bilinear_params dict

Additional bilinear form parameters (if any)

 required
inverse_params_dict dict

Dictionary containing: eps: Diffusion coefficient to be identified

 required

Returns:

Type Description
Tensor

Cell-wise residuals averaged over test functions Shape: (n_cells,)
Note

The weak form includes: - Diffusion term: ∫ε∇u·∇v dΩ where ε is the constant diffusion coefficient to be identified.

Source code in scirex/core/sciml/fastvpinns/physics/poisson2d_inverse.py 
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def pde_loss_poisson_inverse(
    test_shape_val_mat: tf.Tensor,
    test_grad_x_mat: tf.Tensor,
    test_grad_y_mat: tf.Tensor,
    pred_nn: tf.Tensor,
    pred_grad_x_nn: tf.Tensor,
    pred_grad_y_nn: tf.Tensor,
    forcing_function: callable,
    bilinear_params: dict,
    inverse_params_dict: dict,
) -> tf.Tensor:
    """Calculates residual for Poisson inverse problem with constant coefficient.

    Implements the FastVPINNs methodology for computing variational residuals
    in 2D Poisson inverse problems (-∇·(ε∇u) = f) with unknown constant
    diffusion coefficient.

    Args:
        test_shape_val_mat: Test function values at quadrature points
            Shape: (n_elements, n_test_functions, n_quad_points)
        test_grad_x_mat: Test function x-derivatives at quadrature points
            Shape: (n_elements, n_test_functions, n_quad_points)
        test_grad_y_mat: Test function y-derivatives at quadrature points
            Shape: (n_elements, n_test_functions, n_quad_points)
        pred_nn: Neural network solution at quadrature points
            Shape: (n_elements, n_quad_points)
        pred_grad_x_nn: x-derivative of NN solution at quadrature points
            Shape: (n_elements, n_quad_points)
        pred_grad_y_nn: y-derivative of NN solution at quadrature points
            Shape: (n_elements, n_quad_points)
        forcing_function: Right-hand side forcing term
        bilinear_params: Additional bilinear form parameters (if any)
        inverse_params_dict: Dictionary containing:
            eps: Diffusion coefficient to be identified

    Returns:
        Cell-wise residuals averaged over test functions
            Shape: (n_cells,)

    Note:
        The weak form includes:
        - Diffusion term: ∫ε∇u·∇v dΩ
        where ε is the constant diffusion coefficient to be identified.
    """
    # ∫du/dx. dv/dx dΩ
    pde_diffusion_x = tf.transpose(tf.linalg.matvec(test_grad_x_mat, pred_grad_x_nn))

    # ∫du/dy. dv/dy dΩ
    pde_diffusion_y = tf.transpose(tf.linalg.matvec(test_grad_y_mat, pred_grad_y_nn))

    # eps * ∫ (du/dx. dv/dx + du/dy. dv/dy) dΩ
    pde_diffusion = inverse_params_dict["eps"] * (pde_diffusion_x + pde_diffusion_y)

    residual_matrix = pde_diffusion - forcing_function

    residual_cells = tf.reduce_mean(tf.square(residual_matrix), axis=0)

    return residual_cells