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/problem/abstract_problem.py

@@ -1,6 +1,7 @@
 """ Module for AbstractProblem class """
 from abc import ABCMeta, abstractmethod
 from ..utils import merge_tensors, check_consistency
+import torch
 
 
 class AbstractProblem(metaclass=ABCMeta):
@@ -201,6 +202,36 @@ class AbstractProblem(metaclass=ABCMeta):
             if sorted(self.input_pts[location].labels) ==  sorted(self.input_variables): 
                 self._have_sampled_points[location] = True
 
+    def add_points(self, new_points):
+        """
+        Adding points to the already sampled points
+
+        :param dict new_points: a dictionary with key the location to add the points
+            and values the torch.Tensor points.
+        """
+
+        if sorted(new_points.keys()) !=  sorted(self.conditions):
+            TypeError(f'Wrong locations for new points. Location ',
+                      f'should be in {self.conditions}.')
+            
+        for location in new_points.keys():
+            # extract old and new points
+            old_pts = self.input_pts[location]
+            new_pts = new_points[location]
+
+            # if they don't have the same variables error
+            if sorted(old_pts.labels) !=  sorted(new_pts.labels):
+                TypeError(f'Not matching variables for old and new points '
+                          f'in condition {location}.')
+            if old_pts.labels != new_pts.labels:
+                new_pts = torch.hstack([new_pts.extract([i]) for i in old_pts.labels])
+                new_pts.labels = old_pts.labels
+
+            # merging
+            merged_pts = torch.vstack([old_pts, new_points[location]])
+            merged_pts.labels = old_pts.labels
+            self.input_pts[location] = merged_pts
+
     @property
     def have_sampled_points(self):
         """