Renaming

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

pina/solver/physic_informed_solver/pinn.py

@@ -0,0 +1,118 @@
+""" Module for Physics Informed Neural Network. """
+
+import torch
+
+from .pinn_interface import PINNInterface
+from ..solver import SingleSolverInterface
+from ...problem import InverseProblem
+
+
+class PINN(PINNInterface, SingleSolverInterface):
+    r"""
+    Physics Informed Neural Network (PINN) solver class.
+    This class implements 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 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}\sum_{i=1}^N
+        \mathcal{L}(\mathcal{A}[\mathbf{u}](\mathbf{x}_i)) +
+        \frac{1}{N}\sum_{i=1}^N
+        \mathcal{L}(\mathcal{B}[\mathbf{u}](\mathbf{x}_i))
+
+    where :math:`\mathcal{L}` is a specific loss function,
+    default Mean Square Error:
+
+    .. math::
+        \mathcal{L}(v) = \| v \|^2_2.
+
+    .. seealso::
+
+        **Original reference**: Karniadakis, G. E., Kevrekidis, I. G., Lu, L.,
+        Perdikaris, P., Wang, S., & Yang, L. (2021).
+        Physics-informed machine learning. Nature Reviews Physics, 3, 422-440.
+        DOI: `10.1038 <https://doi.org/10.1038/s42254-021-00314-5>`_.
+    """
+
+    def __init__(self,
+                 problem,
+                 model,
+                 optimizer=None,
+                 scheduler=None,
+                 weighting=None,
+                 loss=None):
+        """
+        :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`.
+        """
+        super().__init__(model=model,
+                         problem=problem,
+                         optimizer=optimizer,
+                         scheduler=scheduler,
+                         weighting=weighting,
+                         loss=loss)
+
+    def loss_phys(self, samples, equation):
+        """
+        Computes the physics loss for the 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
+        """
+        residual = self.compute_residual(samples=samples, equation=equation)
+        loss_value = self.loss(
+            torch.zeros_like(residual, requires_grad=True), residual
+        )
+        return loss_value
+
+    def configure_optimizers(self):
+        """
+        Optimizer configuration for the PINN solver.
+
+        :return: The optimizers and the schedulers
+        :rtype: tuple(list, list)
+        """
+        # If the problem is an InverseProblem, add the unknown parameters
+        # to the parameters to be optimized.
+        self.optimizer.hook(self.model.parameters())
+        if isinstance(self.problem, InverseProblem):
+            self.optimizer.instance.add_param_group(
+                {
+                    "params": [
+                        self._params[var]
+                        for var in self.problem.unknown_variables
+                    ]
+                }
+            )
+        self.scheduler.hook(self.optimizer)
+        return (
+            [self.optimizer.instance],
+            [self.scheduler.instance]
+        )