PointSetBC

class pinnx.icbc.PointSetBC(points, values, batch_size=None, shuffle=True)

Dirichlet boundary condition for a set of points.

Compare the output (that associates with points) with values (target data). If more than one component is provided via a list, the resulting loss will be the addative loss of the provided componets.

Parameters:

• points (Dict[str, Union[Array, ndarray, bool, number, bool, int, float, complex, Quantity]]) – An array of points where the corresponding target values are known and used for training.

• values (Dict[str, Union[Array, ndarray, bool, number, bool, int, float, complex, Quantity]]) – A scalar or a 2D-array of values that gives the exact solution of the problem.

• batch_size (int) – The number of points per minibatch, or None to return all points. This is only supported for the backend PyTorch and PaddlePaddle. Note, If you want to use batch size here, you should also set callback ‘pinnx.callbacks.PDEPointResampler(bc_points=True)’ in training.

• shuffle (bool) – Randomize the order on each pass through the data when batching.

collocation_points(X)

Return the collocation points for boundary conditions.

Parameters:

X – Collocation points.

Returns:

Collocation points for boundary conditions.

error(bc_inputs, bc_outputs, **kwargs)

Returns the loss for each component at the initial or boundary conditions.