Neural Tangent Kernel integration + typo fix (#505)

* NTK weighting + typo fixing
* black code formatter + .rst docs

---------

Co-authored-by: Dario Coscia <dariocos99@gmail.com>

docs/source/_rst/loss/ntk_weighting.rst

@@ -0,0 +1,9 @@
+NeuralTangentKernelWeighting
+=============================
+.. currentmodule:: pina.loss.ntk_weighting
+
+.. automodule:: pina.loss.ntk_weighting
+
+.. autoclass:: NeuralTangentKernelWeighting
+   :members:
+   :show-inheritance:

pina/loss/__init__.py

@@ -6,6 +6,7 @@ __all__ = [
     "PowerLoss",
     "WeightingInterface",
     "ScalarWeighting",
+    "NeuralTangentKernelWeighting",
 ]
 
 from .loss_interface import LossInterface
@@ -13,3 +14,4 @@ from .power_loss import PowerLoss
 from .lp_loss import LpLoss
 from .weighting_interface import WeightingInterface
 from .scalar_weighting import ScalarWeighting
+from .ntk_weighting import NeuralTangentKernelWeighting

pina/loss/ntk_weighting.py

@@ -0,0 +1,71 @@
+"""Module for Neural Tangent Kernel Class"""
+
+import torch
+from torch.nn import Module
+from .weighting_interface import WeightingInterface
+from ..utils import check_consistency
+
+
+class NeuralTangentKernelWeighting(WeightingInterface):
+    """
+    A neural tangent kernel scheme for weighting different losses to
+    boost the convergence.
+
+    .. seealso::
+
+        **Original reference**: Wang, Sifan, Xinling Yu, and
+        Paris Perdikaris. *When and why PINNs fail to train:
+        A neural tangent kernel perspective*. Journal of
+        Computational Physics 449 (2022): 110768.
+        DOI: `10.1016/j.jcp.2021.110768 <https://doi.org/10.1016/j.jcp.2021.110768>`_.
+
+
+
+    """
+
+    def __init__(self, model, alpha=0.5):
+        """
+        Initialization of the :class:`NeuralTangentKernelWeighting` class.
+
+        :param torch.nn.Module model: The neural network model.
+        :param float alpha: The alpha parameter.
+        """
+
+        super().__init__()
+        check_consistency(alpha, float)
+        check_consistency(model, Module)
+        if alpha < 0 or alpha > 1:
+            raise ValueError("alpha should be a value between 0 and 1")
+        self.alpha = alpha
+        self.model = model
+        self.weights = {}
+        self.default_value_weights = 1
+
+    def aggregate(self, losses):
+        """
+        Weights the losses according to the Neural Tangent Kernel
+        algorithm.
+
+        :param dict(torch.Tensor) input: The dictionary of losses.
+        :return: The losses aggregation. It should be a scalar Tensor.
+        :rtype: torch.Tensor
+        """
+        losses_norm = {}
+        for condition in losses:
+            losses[condition].backward(retain_graph=True)
+            grads = []
+            for param in self.model.parameters():
+                grads.append(param.grad.view(-1))
+            grads = torch.cat(grads)
+            losses_norm[condition] = torch.norm(grads)
+        self.weights = {
+            condition: self.alpha
+            * self.weights.get(condition, self.default_value_weights)
+            + (1 - self.alpha)
+            * losses_norm[condition]
+            / sum(losses_norm.values())
+            for condition in losses
+        }
+        return sum(
+            self.weights[condition] * loss for condition, loss in losses.items()
+        )

tests/test_weighting/test_ntk_weighting.py

@@ -0,0 +1,65 @@
+import pytest
+from pina import Trainer
+from pina.solver import PINN
+from pina.model import FeedForward
+from pina.problem.zoo import Poisson2DSquareProblem
+from pina.loss import NeuralTangentKernelWeighting
+
+problem = Poisson2DSquareProblem()
+condition_names = problem.conditions.keys()
+
+
+@pytest.mark.parametrize(
+    "model,alpha",
+    [
+        (
+            FeedForward(
+                len(problem.input_variables), len(problem.output_variables)
+            ),
+            0.5,
+        )
+    ],
+)
+def test_constructor(model, alpha):
+    NeuralTangentKernelWeighting(model=model, alpha=alpha)
+
+
+@pytest.mark.parametrize("model", [0.5])
+def test_wrong_constructor1(model):
+    with pytest.raises(ValueError):
+        NeuralTangentKernelWeighting(model)
+
+
+@pytest.mark.parametrize(
+    "model,alpha",
+    [
+        (
+            FeedForward(
+                len(problem.input_variables), len(problem.output_variables)
+            ),
+            1.2,
+        )
+    ],
+)
+def test_wrong_constructor2(model, alpha):
+    with pytest.raises(ValueError):
+        NeuralTangentKernelWeighting(model, alpha)
+
+
+@pytest.mark.parametrize(
+    "model,alpha",
+    [
+        (
+            FeedForward(
+                len(problem.input_variables), len(problem.output_variables)
+            ),
+            0.5,
+        )
+    ],
+)
+def test_train_aggregation(model, alpha):
+    weighting = NeuralTangentKernelWeighting(model=model, alpha=alpha)
+    problem.discretise_domain(50)
+    solver = PINN(problem=problem, model=model, weighting=weighting)
+    trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu")
+    trainer.train()