PINN variants addition and Solvers Update (#263)

* gpinn/basepinn new classes, pinn restructure
* codacy fix gpinn/basepinn/pinn
* inverse problem fix
* Causal PINN (#267)
* fix GPU training in inverse problem (#283)
* Create a `compute_residual` attribute for `PINNInterface`
* Modify dataloading in solvers (#286)
* Modify PINNInterface by removing _loss_phys, _loss_data
* Adding in PINNInterface a variable to track the current condition during training
* Modify GPINN,PINN,CausalPINN to match changes in PINNInterface
* Competitive Pinn Addition (#288)
* fixing after rebase/ fix loss
* fixing final issues

---------

Co-authored-by: Dario Coscia <dariocoscia@Dario-Coscia.local>

* Modify min max formulation to max min for paper consistency
* Adding SAPINN solver (#291)
* rom solver
* fix import

---------

Co-authored-by: Dario Coscia <dariocoscia@Dario-Coscia.local>
Co-authored-by: Anna Ivagnes <75523024+annaivagnes@users.noreply.github.com>
Co-authored-by: valc89 <103250118+valc89@users.noreply.github.com>
Co-authored-by: Monthly Tag bot <mtbot@noreply.github.com>
Co-authored-by: Nicola Demo <demo.nicola@gmail.com>

pina/trainer.py

@@ -1,5 +1,6 @@
 """ Trainer module. """
 
+import torch
 import pytorch_lightning
 from .utils import check_consistency
 from .dataset import SamplePointDataset, SamplePointLoader, DataPointDataset
@@ -63,6 +64,12 @@ class Trainer(pytorch_lightning.Trainer):
         self._loader = SamplePointLoader(
             dataset_phys, dataset_data, batch_size=self.batch_size, shuffle=True
         )
+        pb = self._model.problem
+        if hasattr(pb, "unknown_parameters"):
+            for key in pb.unknown_parameters:
+                pb.unknown_parameters[key] = torch.nn.Parameter(pb.unknown_parameters[key].data.to(device))
+
+
 
     def train(self, **kwargs):
         """