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fix rendering part 2

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giovanni
2025-03-14 00:10:18 +01:00
committed by Nicola Demo
parent e0ad4dc8a0
commit d2e3f458ab
17 changed files with 217 additions and 147 deletions
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pina/solver/physic_informed_solver/causal_pinn.py
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@@ -53,14 +53,16 @@ class CausalPINN(PINN):
.. 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>`_.
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 is only compatible with problems that inherit from the
:class:`~pina.problem.TimeDependentProblem` class.
:class:`~pina.problem.time_dependent_problem.TimeDependentProblem`
class.
"""
def __init__(
@@ -77,17 +79,19 @@ class CausalPINN(PINN):
Initialization of the :class:`CausalPINN` class.
:param AbstractProblem problem: The problem to be solved. It must
inherit from at least :class:`~pina.problem.TimeDependentProblem`.
inherit from at least
:class:`~pina.problem.time_dependent_problem.TimeDependentProblem`.
:param torch.nn.Module model: The neural network model to be used.
:param torch.optim.Optimizer optimizer: The optimizer to be used
If `None`, the Adam optimizer is used. Default is ``None``.
:param torch.optim.LRScheduler scheduler: Learning rate scheduler.
If `None`, the constant learning rate scheduler is used.
:param Optimizer optimizer: The optimizer to be used.
If `None`, the :class:`torch.optim.Adam` optimizer is used.
Default is ``None``.
:param torch.optim.LRScheduler scheduler: Learning rate scheduler.
If `None`, the :class:`torch.optim.lr_scheduler.ConstantLR`
scheduler is used. Default is ``None``.
:param WeightingInterface weighting: The weighting schema to be used.
If `None`, no weighting schema is used. Default is ``None``.
:param torch.nn.Module loss: The loss function to be minimized.
If `None`, the Mean Squared Error (MSE) loss is used.
If `None`, the :class:`torch.nn.MSELoss` loss is used.
Default is `None`.
:param float eps: The exponential decay parameter. Default is ``100``.
:raises ValueError: If the problem is not a TimeDependentProblem.