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

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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

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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/model/avno.py

@@ -110,9 +110,9 @@ class AveragingNeuralOperator(KernelNeuralOperator):
         """
         points_tmp = x.extract(self.coordinates_indices)
         new_batch = x.extract(self.field_indices)
-        new_batch = concatenate((new_batch, points_tmp), dim=2)
+        new_batch = concatenate((new_batch, points_tmp), dim=-1)
         new_batch = self._lifting_operator(new_batch)
         new_batch = self._integral_kernels(new_batch)
-        new_batch = concatenate((new_batch, points_tmp), dim=2)
+        new_batch = concatenate((new_batch, points_tmp), dim=-1)
         new_batch = self._projection_operator(new_batch)
         return new_batch