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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/reduced_order_model.py
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@@ -45,8 +45,8 @@ class ReducedOrderModelSolver(SupervisedSolver):
.. seealso::
**Original reference**: Hesthaven, Jan S., and Stefano Ubbiali.
"Non-intrusive reduced order modeling of nonlinear problems using
neural networks."
*Non-intrusive reduced order modeling of nonlinear problems using
neural networks.*
Journal of Computational Physics 363 (2018): 55-78.
DOI `10.1016/j.jcp.2018.02.037
<https://doi.org/10.1016/j.jcp.2018.02.037>`_.
@@ -67,8 +67,8 @@ class ReducedOrderModelSolver(SupervisedSolver):
..seealso::
**Original reference**: Pichi, Federico, Beatriz Moya, and Jan S.
Hesthaven.
"A graph convolutional autoencoder approach to model order reduction
for parametrized PDEs."
*A graph convolutional autoencoder approach to model order reduction
for parametrized PDEs.*
Journal of Computational Physics 501 (2024): 112762.
DOI `10.1016/j.jcp.2024.112762
<https://doi.org/10.1016/j.jcp.2024.112762>`_.
@@ -105,10 +105,11 @@ class ReducedOrderModelSolver(SupervisedSolver):
If `None`, the :class:`torch.nn.MSELoss` loss is used.
Default is `None`.
:param Optimizer optimizer: The optimizer to be used.
If `None`, the :class:`torch.optim.Adam`. optimizer is used.
If `None`, the :class:`torch.optim.Adam` optimizer is used.
Default is ``None``.
:param Scheduler scheduler: Learning rate scheduler. If `None`,
the constant learning rate scheduler is used. Default is ``None``.
:param Scheduler 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 bool use_lt: If ``True``, the solver uses LabelTensors as input.
@@ -152,9 +153,10 @@ class ReducedOrderModelSolver(SupervisedSolver):
of the ``interpolation_network`` on the input, and maps it to output
space by calling the decode methode of the ``reduction_network``.
:param torch.Tensor x: Input tensor.
:param x: Input tensor.
:type x: torch.Tensor | LabelTensor
:return: Solver solution.
:rtype: torch.Tensor
:rtype: torch.Tensor | LabelTensor
"""
reduction_network = self.model["reduction_network"]
interpolation_network = self.model["interpolation_network"]