Renaming

* solvers -> solver
* adaptive_functions -> adaptive_function
* callbacks -> callback
* operators -> operator
* pinns -> physics_informed_solver
* layers -> block

pina/model/avno.py

@@ -2,9 +2,9 @@
 
 import torch
 from torch import nn, cat
-from .layers import AVNOBlock
+from .block import AVNOBlock
 from .base_no import KernelNeuralOperator
-from pina.utils import check_consistency
+from ..utils import check_consistency
 
 
 class AveragingNeuralOperator(KernelNeuralOperator):

pina/model/base_no.py

@@ -3,14 +3,14 @@ Kernel Neural Operator Module.
 """
 
 import torch
-from pina.utils import check_consistency
+from ..utils import check_consistency
 
 
 class KernelNeuralOperator(torch.nn.Module):
     r"""
     Base class for composing Neural Operators with integral kernels.
 
-    This is a base class for composing neural operators with multiple
+    This is a base class for composing neural operator with multiple
     integral kernels. All neural operator models defined in PINA inherit
     from this class. The structure is inspired by the work of Kovachki, N.
     et al. see Figure 2 of the reference for extra details. The Neural

pina/model/block/__init__.py

@@ -0,0 +1,35 @@
+__all__ = [
+    "ContinuousConvBlock",
+    "ResidualBlock",
+    "EnhancedLinear",
+    "SpectralConvBlock1D",
+    "SpectralConvBlock2D",
+    "SpectralConvBlock3D",
+    "FourierBlock1D",
+    "FourierBlock2D",
+    "FourierBlock3D",
+    "PODBlock",
+    "OrthogonalBlock",
+    "PeriodicBoundaryEmbedding",
+    "FourierFeatureEmbedding",
+    "AVNOBlock",
+    "LowRankBlock",
+    "RBFBlock",
+    "GNOBlock"
+]
+
+from .convolution_2d import ContinuousConvBlock
+from .residual import ResidualBlock, EnhancedLinear
+from .spectral import (
+    SpectralConvBlock1D,
+    SpectralConvBlock2D,
+    SpectralConvBlock3D,
+)
+from .fourier import FourierBlock1D, FourierBlock2D, FourierBlock3D
+from .pod import PODBlock
+from .orthogonal import OrthogonalBlock
+from .embedding import PeriodicBoundaryEmbedding, FourierFeatureEmbedding
+from .avno_layer import AVNOBlock
+from .lowrank_layer import LowRankBlock
+from .rbf_layer import RBFBlock
+from .gno_block import GNOBlock

pina/model/block/fourier.py

@@ -2,7 +2,7 @@ import torch
 import torch.nn as nn
 from ...utils import check_consistency
 
-from pina.model.layers import (
+from pina.model.block import (
     SpectralConvBlock1D,
     SpectralConvBlock2D,
     SpectralConvBlock3D,

pina/model/deeponet.py

@@ -18,7 +18,7 @@ class MIONet(torch.nn.Module):
     .. seealso::
 
         **Original reference**: Jin, Pengzhan, Shuai Meng, and Lu Lu.
-        *MIONet: Learning multiple-input operators via tensor product.*
+        *MIONet: Learning multiple-input operator via tensor product.*
         SIAM Journal on Scientific Computing 44.6 (2022): A3490-A351
         DOI: `10.1137/22M1477751
         <https://doi.org/10.1137/22M1477751>`_
@@ -289,8 +289,8 @@ class DeepONet(MIONet):
     .. seealso::
 
         **Original reference**: Lu, L., Jin, P., Pang, G. et al. *Learning
-        nonlinear operators via DeepONet based on the universal approximation
-        theorem of operators*. Nat Mach Intell 3, 218–229 (2021).
+        nonlinear operator 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/feed_forward.py

@@ -3,7 +3,7 @@
 import torch
 import torch.nn as nn
 from ..utils import check_consistency
-from .layers.residual import EnhancedLinear
+from .block.residual import EnhancedLinear
 
 
 class FeedForward(torch.nn.Module):

pina/model/fno.py

@@ -7,7 +7,7 @@ import torch.nn as nn
 from ..label_tensor import LabelTensor
 import warnings
 from ..utils import check_consistency
-from .layers.fourier import FourierBlock1D, FourierBlock2D, FourierBlock3D
+from .block.fourier import FourierBlock1D, FourierBlock2D, FourierBlock3D
 from .base_no import KernelNeuralOperator
 
 

pina/model/gno.py

@@ -1,6 +1,6 @@
 import torch
 from torch.nn import Tanh
-from .layers import GNOBlock
+from .block import GNOBlock
 from .base_no import KernelNeuralOperator
 
 

pina/model/layers/__init__.py

@@ -1,35 +0,0 @@
-__all__ = [
-    "ContinuousConvBlock",
-    "ResidualBlock",
-    "EnhancedLinear",
-    "SpectralConvBlock1D",
-    "SpectralConvBlock2D",
-    "SpectralConvBlock3D",
-    "FourierBlock1D",
-    "FourierBlock2D",
-    "FourierBlock3D",
-    "PODBlock",
-    "OrthogonalBlock",
-    "PeriodicBoundaryEmbedding",
-    "FourierFeatureEmbedding",
-    "AVNOBlock",
-    "LowRankBlock",
-    "RBFBlock",
-    "GNOBlock"
-]
-
-from .convolution_2d import ContinuousConvBlock
-from .residual import ResidualBlock, EnhancedLinear
-from .spectral import (
-    SpectralConvBlock1D,
-    SpectralConvBlock2D,
-    SpectralConvBlock3D,
-)
-from .fourier import FourierBlock1D, FourierBlock2D, FourierBlock3D
-from .pod import PODBlock
-from .orthogonal import OrthogonalBlock
-from .embedding import PeriodicBoundaryEmbedding, FourierFeatureEmbedding
-from .avno_layer import AVNOBlock
-from .lowrank_layer import LowRankBlock
-from .rbf_layer import RBFBlock
-from .gno_block import GNOBlock

pina/model/lno.py

@@ -3,10 +3,10 @@
 import torch
 from torch import nn, cat
 
-from pina.utils import check_consistency
+from ..utils import check_consistency
 
 from .base_no import KernelNeuralOperator
-from .layers.lowrank_layer import LowRankBlock
+from .block.lowrank_layer import LowRankBlock
 
 
 class LowRankNeuralOperator(KernelNeuralOperator):
@@ -19,7 +19,7 @@ class LowRankNeuralOperator(KernelNeuralOperator):
     to other functions. It can be trained with Supervised or PINN based
     learning strategies.
     LowRankNeuralOperator does convolution by performing a low rank
-    approximation, see :class:`~pina.model.layers.lowrank_layer.LowRankBlock`.
+    approximation, see :class:`~pina.model.block.lowrank_layer.LowRankBlock`.
 
     .. seealso::
 

pina/model/spline.py

@@ -1,7 +1,6 @@
 """Module for Spline model"""
 
 import torch
-import torch.nn as nn
 from ..utils import check_consistency