* Adding Collector for handling data sampling/collection before dataset/dataloader

* Modify domain by adding sample_mode, variables as property
* Small change concatenate -> cat in lno/avno
* Create different factory classes for conditions

pina/model/avno.py

@@ -1,7 +1,7 @@
 """Module Averaging Neural Operator."""
 
 import torch
-from torch import nn, concatenate
+from torch import nn, cat
 from .layers import AVNOBlock
 from .base_no import KernelNeuralOperator
 from pina.utils import check_consistency
@@ -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=-1)
+        new_batch = cat((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=-1)
+        new_batch = cat((new_batch, points_tmp), dim=-1)
         new_batch = self._projection_operator(new_batch)
         return new_batch

pina/model/lno.py

@@ -1,7 +1,7 @@
 """Module LowRank Neural Operator."""
 
 import torch
-from torch import nn, concatenate
+from torch import nn, cat
 
 from pina.utils import check_consistency
 
@@ -145,4 +145,4 @@ class LowRankNeuralOperator(KernelNeuralOperator):
         for module in self._integral_kernels:
             x = module(x, coords)
         # projecting
-        return self._projection_operator(concatenate((x, coords), dim=-1))
+        return self._projection_operator(cat((x, coords), dim=-1))