renaming

Author: GiovanniCanali

pina/callback/processing_callback.py

@@ -92,7 +92,7 @@ def __init__(self, metrics="val", **kwargs):
         Example:
             >>> pbar = PINAProgressBar(['mean'])
             >>> # ... Perform training ...
-            >>> trainer = Trainer(solver, callback=[pbar])
+            >>> trainer = Trainer(solver, callbacks=[pbar])
         """
         super().__init__(**kwargs)
         # check consistency

pina/model/block/__init__.py

@@ -30,6 +30,6 @@
 from .orthogonal import OrthogonalBlock
 from .embedding import PeriodicBoundaryEmbedding, FourierFeatureEmbedding
 from .average_neural_operator_block import AVNOBlock
-from .lowrank_block import LowRankBlock
+from .low_rank_block import LowRankBlock
 from .rbf_block import RBFBlock
 from .gno_block import GNOBlock

pina/model/block/lowrank_block.py pina/model/block/low_rank_block.pypina/model/block/lowrank_block.py renamed to pina/model/block/low_rank_block.py

@@ -18,7 +18,7 @@ class MIONet(torch.nn.Module):
     .. seealso::
 
         **Original reference**: Jin, Pengzhan, Shuai Meng, and Lu Lu.
-        *MIONet: Learning multiple-input operator via tensor product.*
+        *MIONet: Learning multiple-input operators via tensor product.*
         SIAM Journal on Scientific Computing 44.6 (2022): A3490-A351
         DOI: `10.1137/22M1477751
         <https://doi.org/10.1137/22M1477751>`_
@@ -289,7 +289,7 @@ class DeepONet(MIONet):
     .. seealso::
 
         **Original reference**: Lu, L., Jin, P., Pang, G. et al. *Learning
-        nonlinear operator via DeepONet based on the universal approximation
+        nonlinear operators via DeepONet based on the universal approximation
         theorem of operator*. Nat Mach Intell 3, 218–229 (2021).
         DOI: `10.1038/s42256-021-00302-5
         <https://doi.org/10.1038/s42256-021-00302-5>`_

pina/model/deeponet.py

@@ -6,7 +6,7 @@
 from ..utils import check_consistency
 
 from .kernel_neural_operator import KernelNeuralOperator
-from .block.lowrank_block import LowRankBlock
+from .block.low_rank_block import LowRankBlock
 
 
 class LowRankNeuralOperator(KernelNeuralOperator):