sup3r.utilities.loss_metrics.MaterialDerivativeLoss

sup3r.utilities.loss_metrics.MaterialDerivativeLoss#

class MaterialDerivativeLoss(input_features)[source]#

Bases: PhysicsBasedLoss

Loss class for the material derivative. This is the left hand side of the Navier-Stokes equation and is equal to internal + external forces divided by density in general. Under certain simplifying assumptions, this is equal to zero.

References

https://en.wikipedia.org/wiki/Material_derivative

Initialize the loss with given input features

Parameters:

input_features (list | str) – List of input features that the loss metric will be calculated on. This is meant to be used for physics-based loss metrics that require specific input features. If ‘all’, the loss will be calculated on all features. Otherwise, the loss will be calculated on the features specified in the list. The order of features in the list will be checked to determine the order of features in the input tensors.

Methods

call(y_true, y_pred)

Invokes the Loss instance.

from_config(config)

Instantiates a Loss from its config (output of get_config()).

get_config()

Returns the config dictionary for a Loss instance.

Attributes

LOSS_METRIC

__call__(x1, x2)[source]#

Custom content loss that encourages accuracy of the material derivative.

Parameters:

  • x1 (tf.tensor) – synthetic generator output (n_observations, spatial_1, spatial_2, temporal, features)

  • x2 (tf.tensor) – high resolution data (n_observations, spatial_1, spatial_2, temporal, features)

Returns:

tf.tensor – 0D tensor with loss value

abstract call(y_true, y_pred)#

Invokes the Loss instance.

Args:
y_true: Ground truth values. shape = [batch_size, d0, .. dN],

except sparse loss functions such as sparse categorical crossentropy where shape = [batch_size, d0, .. dN-1]

y_pred: The predicted values. shape = [batch_size, d0, .. dN]

Returns:

Loss values with the shape [batch_size, d0, .. dN-1].

classmethod from_config(config)#

Instantiates a Loss from its config (output of get_config()).

Args:

config: Output of get_config().

Returns:

A Loss instance.

get_config()#

Returns the config dictionary for a Loss instance.

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