Renaming

* solvers -> solver
* adaptive_functions -> adaptive_function
* callbacks -> callback
* operators -> operator
* pinns -> physics_informed_solver
* layers -> block

pina/solver/physic_informed_solver/causal_pinn.py

@@ -0,0 +1,207 @@
+""" Module for Causal PINN. """
+
+import torch
+
+from pina.problem import TimeDependentProblem
+from .pinn import PINN
+from pina.utils import check_consistency
+
+
+class CausalPINN(PINN):
+    r"""
+    Causal Physics Informed Neural Network (CausalPINN) solver class.
+    This class implements Causal Physics Informed Neural
+    Network solver, using a user specified ``model`` to solve a specific
+    ``problem``. It can be used for solving both forward and inverse problems.
+
+    The Causal Physics Informed Network aims to find
+    the solution :math:`\mathbf{u}:\Omega\rightarrow\mathbb{R}^m`
+    of the differential problem:
+
+    .. math::
+
+        \begin{cases}
+        \mathcal{A}[\mathbf{u}](\mathbf{x})=0\quad,\mathbf{x}\in\Omega\\
+        \mathcal{B}[\mathbf{u}](\mathbf{x})=0\quad,
+        \mathbf{x}\in\partial\Omega
+        \end{cases}
+
+    minimizing the loss function
+
+    .. math::
+        \mathcal{L}_{\rm{problem}} = \frac{1}{N_t}\sum_{i=1}^{N_t}
+        \omega_{i}\mathcal{L}_r(t_i),
+
+    where:
+
+    .. math::
+        \mathcal{L}_r(t) = \frac{1}{N}\sum_{i=1}^N
+        \mathcal{L}(\mathcal{A}[\mathbf{u}](\mathbf{x}_i, t)) +
+        \frac{1}{N}\sum_{i=1}^N
+        \mathcal{L}(\mathcal{B}[\mathbf{u}](\mathbf{x}_i, t))
+
+    and,
+
+    .. math::
+        \omega_i = \exp\left(\epsilon \sum_{k=1}^{i-1}\mathcal{L}_r(t_k)\right).
+
+    :math:`\epsilon` is an hyperparameter, default set to :math:`100`, while
+    :math:`\mathcal{L}` is a specific loss function,
+    default Mean Square Error:
+
+    .. math::
+        \mathcal{L}(v) = \| v \|^2_2.
+
+
+    .. seealso::
+
+        **Original reference**: Wang, Sifan, Shyam Sankaran, and Paris
+        Perdikaris. "Respecting causality for training physics-informed
+        neural networks." Computer Methods in Applied Mechanics
+        and Engineering 421 (2024): 116813.
+        DOI `10.1016 <https://doi.org/10.1016/j.cma.2024.116813>`_.
+
+    .. note::
+        This class can only work for problems inheriting
+        from at least
+        :class:`~pina.problem.timedep_problem.TimeDependentProblem` class.
+    """
+
+    def __init__(self,
+                 problem,
+                 model,
+                 optimizer=None,
+                 scheduler=None,
+                 weighting=None,
+                 loss=None,
+                 eps=100):
+        """
+        :param torch.nn.Module model: The neural network model to use.
+        :param AbstractProblem problem: The formulation of the problem.
+        :param torch.optim.Optimizer optimizer: The neural network optimizer to
+            use; default `None`.
+        :param torch.optim.LRScheduler scheduler: Learning rate scheduler;
+            default `None`.
+        :param WeightingInterface weighting: The weighting schema to use;
+            default `None`.
+        :param torch.nn.Module loss: The loss function to be minimized;
+            default `None`.
+        :param float eps: The exponential decay parameter; default `100`.
+        """
+        super().__init__(model=model,
+                         problem=problem,
+                         optimizer=optimizer,
+                         scheduler=scheduler,
+                         weighting=weighting,
+                         loss=loss)
+
+        # checking consistency
+        check_consistency(eps, (int, float))
+        self._eps = eps
+        if not isinstance(self.problem, TimeDependentProblem):
+            raise ValueError(
+                "Casual PINN works only for problems"
+                "inheriting from TimeDependentProblem."
+            )
+
+    def loss_phys(self, samples, equation):
+        """
+        Computes the physics loss for the Causal PINN solver based on given
+        samples and equation.
+
+        :param LabelTensor samples: The samples to evaluate the physics loss.
+        :param EquationInterface equation: The governing equation
+            representing the physics.
+        :return: The physics loss calculated based on given
+            samples and equation.
+        :rtype: LabelTensor
+        """
+        # split sequentially ordered time tensors into chunks
+        chunks, labels = self._split_tensor_into_chunks(samples)
+        # compute residuals - this correspond to ordered loss functions
+        # values for each time step. Apply `flatten` to ensure obtaining
+        # a tensor of shape #chunks after concatenating the residuals
+        time_loss = []
+        for chunk in chunks:
+            chunk.labels = labels
+            # classical PINN loss
+            residual = self.compute_residual(samples=chunk, equation=equation)
+            loss_val = self.loss(
+                torch.zeros_like(residual, requires_grad=True), residual
+            )
+            time_loss.append(loss_val)
+
+        # concatenate residuals
+        time_loss = torch.stack(time_loss)
+        # compute weights without storing the gradient
+        with torch.no_grad():
+            weights = self._compute_weights(time_loss)
+        return (weights * time_loss).mean()
+
+    @property
+    def eps(self):
+        """
+        The exponential decay parameter.
+        """
+        return self._eps
+
+    @eps.setter
+    def eps(self, value):
+        """
+        Setter method for the eps parameter.
+
+        :param float value: The exponential decay parameter.
+        """
+        check_consistency(value, float)
+        self._eps = value
+
+    def _sort_label_tensor(self, tensor):
+        """
+        Sorts the label tensor based on time variables.
+
+        :param LabelTensor tensor: The label tensor to be sorted.
+        :return: The sorted label tensor based on time variables.
+        :rtype: LabelTensor
+        """
+        # labels input tensors
+        labels = tensor.labels
+        # extract time tensor
+        time_tensor = tensor.extract(self.problem.temporal_domain.variables)
+        # sort the time tensors (this is very bad for GPU)
+        _, idx = torch.sort(time_tensor.tensor.flatten())
+        tensor = tensor[idx]
+        tensor.labels = labels
+        return tensor
+
+    def _split_tensor_into_chunks(self, tensor):
+        """
+        Splits the label tensor into chunks based on time.
+
+        :param LabelTensor tensor: The label tensor to be split.
+        :return: Tuple containing the chunks and the original labels.
+        :rtype: Tuple[List[LabelTensor], List]
+        """
+        # extract labels
+        labels = tensor.labels
+        # sort input tensor based on time
+        tensor = self._sort_label_tensor(tensor)
+        # extract time tensor
+        time_tensor = tensor.extract(self.problem.temporal_domain.variables)
+        # count unique tensors in time
+        _, idx_split = time_tensor.unique(return_counts=True)
+        # split the tensor based on time
+        chunks = torch.split(tensor, tuple(idx_split))
+        return chunks, labels
+
+    def _compute_weights(self, loss):
+        """
+        Computes the weights for the physics loss based on the cumulative loss.
+
+        :param LabelTensor loss: The physics loss values.
+        :return: The computed weights for the physics loss.
+        :rtype: LabelTensor
+        """
+        # compute comulative loss and multiply by epsilon
+        cumulative_loss = self._eps * torch.cumsum(loss, dim=0)
+        # return the exponential of the negative weighted cumulative sum
+        return torch.exp(-cumulative_loss)