Adaptive Refinment and Multiple Optimizer callbacks

* Implementing a callback to switch between optimizers during training
* Implementing the R3Refinment for collocation points
* Modify trainer -> dataloader is created or updated by calling `_create_or_update_loader`
* Adding `add_points` routine to AbstractProblem so that new points can be added without resampling from scratch

pina/trainer.py

@@ -9,9 +9,6 @@ class Trainer(pl.Trainer):
 
     def __init__(self, solver, **kwargs):
         super().__init__(**kwargs)
-        
-        # get accellerator
-        device = self._accelerator_connector._accelerator_flag
 
         # check inheritance consistency for solver
         check_consistency(solver, SolverInterface)
@@ -26,8 +23,15 @@ class Trainer(pl.Trainer):
                                'in the provided locations.')
         
         # TODO: make a better dataloader for train
-        self._loader = DummyLoader(solver.problem.input_pts, device) 
+        self._create_or_update_loader()
 
+    # this method is used here because is resampling is needed
+    # during training, there is no need to define to touch the
+    # trainer dataloader, just call the method.
+    def _create_or_update_loader(self):
+        # get accellerator
+        device = self._accelerator_connector._accelerator_flag
+        self._loader = DummyLoader(self._model.problem.input_pts, device) 
 
     def train(self, **kwargs): # TODO add kwargs and lightining capabilities
         return super().fit(self._model, self._loader, **kwargs)