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neuraloperator · Dec 20, 2024 · 7591ba4 · 7591ba4
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diff --git a/dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip b/dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip
diff --git a/dev/_downloads/0d78e075dd52a34e158d7f5f710dfe89/plot_incremental_FNO_darcy.zip b/dev/_downloads/0d78e075dd52a34e158d7f5f710dfe89/plot_incremental_FNO_darcy.zip
diff --git a/dev/_downloads/20c43dd37baf603889c4dc23e93bdb60/plot_count_flops.zip b/dev/_downloads/20c43dd37baf603889c4dc23e93bdb60/plot_count_flops.zip
diff --git a/dev/_downloads/3864a2d85c7ce11adeac9580559229ab/plot_darcy_flow.zip b/dev/_downloads/3864a2d85c7ce11adeac9580559229ab/plot_darcy_flow.zip
diff --git a/dev/_downloads/3faf9d2eaee5cc8e9f1c631c002ce544/plot_darcy_flow_spectrum.zip b/dev/_downloads/3faf9d2eaee5cc8e9f1c631c002ce544/plot_darcy_flow_spectrum.zip
diff --git a/dev/_downloads/4e1cba46bb51062a073424e1efbaad57/plot_DISCO_convolutions.zip b/dev/_downloads/4e1cba46bb51062a073424e1efbaad57/plot_DISCO_convolutions.zip
diff --git a/dev/_downloads/5e60095ce99919773daa83384f767e02/plot_SFNO_swe.zip b/dev/_downloads/5e60095ce99919773daa83384f767e02/plot_SFNO_swe.zip
diff --git a/dev/_downloads/645da00b8fbbb9bb5cae877fd0f31635/plot_FNO_darcy.zip b/dev/_downloads/645da00b8fbbb9bb5cae877fd0f31635/plot_FNO_darcy.zip
diff --git a/dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip b/dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip
diff --git a/dev/_downloads/7296405f6df7c2cfe184e9b258cee33e/checkpoint_FNO_darcy.zip b/dev/_downloads/7296405f6df7c2cfe184e9b258cee33e/checkpoint_FNO_darcy.zip
diff --git a/dev/_downloads/cefc537c5730a6b3e916b83c1fd313d6/plot_UNO_darcy.zip b/dev/_downloads/cefc537c5730a6b3e916b83c1fd313d6/plot_UNO_darcy.zip
diff --git a/dev/_images/sphx_glr_plot_SFNO_swe_001.png b/dev/_images/sphx_glr_plot_SFNO_swe_001.png
diff --git a/dev/_images/sphx_glr_plot_SFNO_swe_thumb.png b/dev/_images/sphx_glr_plot_SFNO_swe_thumb.png
diff --git a/dev/_images/sphx_glr_plot_UNO_darcy_001.png b/dev/_images/sphx_glr_plot_UNO_darcy_001.png
diff --git a/dev/_images/sphx_glr_plot_UNO_darcy_thumb.png b/dev/_images/sphx_glr_plot_UNO_darcy_thumb.png
diff --git a/dev/_sources/auto_examples/plot_DISCO_convolutions.rst.txt b/dev/_sources/auto_examples/plot_DISCO_convolutions.rst.txt
@@ -357,7 +357,7 @@ in order to compute the convolved image, we need to first bring it into the righ
  .. code-block:: none
 
 
-    <matplotlib.colorbar.Colorbar object at 0x7f32ac13c550>
+    <matplotlib.colorbar.Colorbar object at 0x7fe4cde24550>
 
 
 
@@ -448,7 +448,7 @@ in order to compute the convolved image, we need to first bring it into the righ
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 31.554 seconds)
+   **Total running time of the script:** (0 minutes 31.319 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_DISCO_convolutions.py:

diff --git a/dev/_sources/auto_examples/plot_FNO_darcy.rst.txt b/dev/_sources/auto_examples/plot_FNO_darcy.rst.txt
@@ -248,13 +248,13 @@ Training the model
     )
 
     ### SCHEDULER ###
-     <torch.optim.lr_scheduler.CosineAnnealingLR object at 0x7f32a9cc6ad0>
+     <torch.optim.lr_scheduler.CosineAnnealingLR object at 0x7fe4e07b2ad0>
 
     ### LOSSES ###
 
-     * Train: <neuralop.losses.data_losses.H1Loss object at 0x7f32a9cc6850>
+     * Train: <neuralop.losses.data_losses.H1Loss object at 0x7fe4e07b2850>
 
-     * Test: {'h1': <neuralop.losses.data_losses.H1Loss object at 0x7f32a9cc6850>, 'l2': <neuralop.losses.data_losses.LpLoss object at 0x7f32a9cc6490>}
+     * Test: {'h1': <neuralop.losses.data_losses.H1Loss object at 0x7fe4e07b2850>, 'l2': <neuralop.losses.data_losses.LpLoss object at 0x7fe4e07b2210>}
 
 
 
@@ -313,20 +313,20 @@ Then train the model on our small Darcy-Flow dataset:
     Raw outputs of shape torch.Size([32, 1, 16, 16])
     [0] time=2.57, avg_loss=0.6956, train_err=21.7383
     Eval: 16_h1=0.4298, 16_l2=0.3487, 32_h1=0.5847, 32_l2=0.3542
-    [3] time=2.56, avg_loss=0.2103, train_err=6.5705
+    [3] time=2.55, avg_loss=0.2103, train_err=6.5705
     Eval: 16_h1=0.2030, 16_l2=0.1384, 32_h1=0.5075, 32_l2=0.1774
-    [6] time=2.59, avg_loss=0.1911, train_err=5.9721
+    [6] time=2.56, avg_loss=0.1911, train_err=5.9721
     Eval: 16_h1=0.2099, 16_l2=0.1374, 32_h1=0.4907, 32_l2=0.1783
-    [9] time=2.57, avg_loss=0.1410, train_err=4.4073
+    [9] time=2.56, avg_loss=0.1410, train_err=4.4073
     Eval: 16_h1=0.2052, 16_l2=0.1201, 32_h1=0.5268, 32_l2=0.1615
-    [12] time=2.55, avg_loss=0.1422, train_err=4.4434
+    [12] time=2.56, avg_loss=0.1422, train_err=4.4434
     Eval: 16_h1=0.2131, 16_l2=0.1285, 32_h1=0.5413, 32_l2=0.1741
-    [15] time=2.57, avg_loss=0.1198, train_err=3.7424
+    [15] time=2.62, avg_loss=0.1198, train_err=3.7424
     Eval: 16_h1=0.1984, 16_l2=0.1137, 32_h1=0.5255, 32_l2=0.1569
-    [18] time=2.55, avg_loss=0.1104, train_err=3.4502
+    [18] time=2.57, avg_loss=0.1104, train_err=3.4502
     Eval: 16_h1=0.2039, 16_l2=0.1195, 32_h1=0.5062, 32_l2=0.1603
 
-    {'train_err': 2.9605126455426216, 'avg_loss': 0.0947364046573639, 'avg_lasso_loss': None, 'epoch_train_time': 2.5465377909999916}
+    {'train_err': 2.9605126455426216, 'avg_loss': 0.0947364046573639, 'avg_lasso_loss': None, 'epoch_train_time': 2.5713351109999394}
 
 
 
@@ -476,7 +476,7 @@ are other ways to scale the outputs of the FNO to train a true super-resolution
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 52.494 seconds)
+   **Total running time of the script:** (0 minutes 52.622 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_FNO_darcy.py:

diff --git a/dev/_sources/auto_examples/plot_SFNO_swe.rst.txt b/dev/_sources/auto_examples/plot_SFNO_swe.rst.txt
@@ -234,13 +234,13 @@ Creating the losses
     )
 
     ### SCHEDULER ###
-     <torch.optim.lr_scheduler.CosineAnnealingLR object at 0x7f32ba653230>
+     <torch.optim.lr_scheduler.CosineAnnealingLR object at 0x7fe4e0743230>
 
     ### LOSSES ###
 
-     * Train: <neuralop.losses.data_losses.LpLoss object at 0x7f32ba652cf0>
+     * Train: <neuralop.losses.data_losses.LpLoss object at 0x7fe4e0742cf0>
 
-     * Test: {'l2': <neuralop.losses.data_losses.LpLoss object at 0x7f32ba652cf0>}
+     * Test: {'l2': <neuralop.losses.data_losses.LpLoss object at 0x7fe4e0742cf0>}
 
 
 
@@ -297,22 +297,22 @@ Train the model on the spherical SWE dataset
     Training on 200 samples
     Testing on [50, 50] samples         on resolutions [(32, 64), (64, 128)].
     Raw outputs of shape torch.Size([4, 3, 32, 64])
-    [0] time=3.65, avg_loss=2.5842, train_err=10.3366
-    Eval: (32, 64)_l2=1.8738, (64, 128)_l2=2.4284
-    [3] time=3.62, avg_loss=0.4147, train_err=1.6589
-    Eval: (32, 64)_l2=0.5781, (64, 128)_l2=2.3744
-    [6] time=3.63, avg_loss=0.2662, train_err=1.0649
-    Eval: (32, 64)_l2=0.5419, (64, 128)_l2=2.3195
-    [9] time=3.58, avg_loss=0.2207, train_err=0.8829
-    Eval: (32, 64)_l2=0.4887, (64, 128)_l2=2.3423
-    [12] time=3.58, avg_loss=0.1894, train_err=0.7575
-    Eval: (32, 64)_l2=0.4850, (64, 128)_l2=2.3244
-    [15] time=3.57, avg_loss=0.1631, train_err=0.6523
-    Eval: (32, 64)_l2=0.4811, (64, 128)_l2=2.3205
-    [18] time=3.58, avg_loss=0.1442, train_err=0.5769
-    Eval: (32, 64)_l2=0.4802, (64, 128)_l2=2.3157
+    [0] time=3.66, avg_loss=2.6152, train_err=10.4609
+    Eval: (32, 64)_l2=1.8807, (64, 128)_l2=2.5119
+    [3] time=3.64, avg_loss=0.4558, train_err=1.8232
+    Eval: (32, 64)_l2=0.9008, (64, 128)_l2=2.6564
+    [6] time=3.58, avg_loss=0.2771, train_err=1.1085
+    Eval: (32, 64)_l2=0.8429, (64, 128)_l2=2.5842
+    [9] time=3.60, avg_loss=0.2288, train_err=0.9151
+    Eval: (32, 64)_l2=0.8236, (64, 128)_l2=2.5503
+    [12] time=3.59, avg_loss=0.1939, train_err=0.7754
+    Eval: (32, 64)_l2=0.7405, (64, 128)_l2=2.4524
+    [15] time=3.59, avg_loss=0.1673, train_err=0.6693
+    Eval: (32, 64)_l2=0.7308, (64, 128)_l2=2.4187
+    [18] time=3.59, avg_loss=0.1475, train_err=0.5900
+    Eval: (32, 64)_l2=0.6825, (64, 128)_l2=2.3738
 
-    {'train_err': 0.5465761256217957, 'avg_loss': 0.13664403140544892, 'avg_lasso_loss': None, 'epoch_train_time': 3.5847762680000415}
+    {'train_err': 0.5666905903816223, 'avg_loss': 0.14167264759540557, 'avg_lasso_loss': None, 'epoch_train_time': 3.6301156069999934}
 
 
 
@@ -383,7 +383,7 @@ In practice we would train a Neural Operator on one or multiple GPUs
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (1 minutes 27.934 seconds)
+   **Total running time of the script:** (1 minutes 28.624 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_SFNO_swe.py:

diff --git a/dev/_sources/auto_examples/plot_UNO_darcy.rst.txt b/dev/_sources/auto_examples/plot_UNO_darcy.rst.txt
@@ -345,13 +345,13 @@ Creating the losses
     )
 
     ### SCHEDULER ###
-     <torch.optim.lr_scheduler.CosineAnnealingLR object at 0x7f32b8316490>
+     <torch.optim.lr_scheduler.CosineAnnealingLR object at 0x7fe4d9f22490>
 
     ### LOSSES ###
 
-     * Train: <neuralop.losses.data_losses.H1Loss object at 0x7f32bb7fdfd0>
+     * Train: <neuralop.losses.data_losses.H1Loss object at 0x7fe4f729b620>
 
-     * Test: {'h1': <neuralop.losses.data_losses.H1Loss object at 0x7f32bb7fdfd0>, 'l2': <neuralop.losses.data_losses.LpLoss object at 0x7f32b8316210>}
+     * Test: {'h1': <neuralop.losses.data_losses.H1Loss object at 0x7fe4f729b620>, 'l2': <neuralop.losses.data_losses.LpLoss object at 0x7fe4d9f22210>}
 
 
 
@@ -410,22 +410,22 @@ Actually train the model on our small Darcy-Flow dataset
     Training on 1000 samples
     Testing on [50, 50] samples         on resolutions [16, 32].
     Raw outputs of shape torch.Size([32, 1, 16, 16])
-    [0] time=9.95, avg_loss=0.6844, train_err=21.3879
-    Eval: 16_h1=0.6813, 16_l2=0.4521, 32_h1=0.9371, 32_l2=0.6767
-    [3] time=9.89, avg_loss=0.2622, train_err=8.1950
-    Eval: 16_h1=0.2517, 16_l2=0.1695, 32_h1=0.8607, 32_l2=0.6035
-    [6] time=9.92, avg_loss=0.2178, train_err=6.8077
-    Eval: 16_h1=0.2469, 16_l2=0.1539, 32_h1=0.8402, 32_l2=0.5624
-    [9] time=9.88, avg_loss=0.2180, train_err=6.8126
-    Eval: 16_h1=0.2543, 16_l2=0.1619, 32_h1=0.7828, 32_l2=0.5254
-    [12] time=9.94, avg_loss=0.1915, train_err=5.9847
-    Eval: 16_h1=0.2700, 16_l2=0.1719, 32_h1=0.7757, 32_l2=0.4989
-    [15] time=9.87, avg_loss=0.1635, train_err=5.1107
-    Eval: 16_h1=0.2434, 16_l2=0.1517, 32_h1=0.8047, 32_l2=0.5077
-    [18] time=9.90, avg_loss=0.1674, train_err=5.2311
-    Eval: 16_h1=0.2448, 16_l2=0.1587, 32_h1=0.7799, 32_l2=0.5013
+    [0] time=10.04, avg_loss=0.6503, train_err=20.3222
+    Eval: 16_h1=0.3710, 16_l2=0.2491, 32_h1=0.8856, 32_l2=0.5331
+    [3] time=9.99, avg_loss=0.2580, train_err=8.0616
+    Eval: 16_h1=0.3135, 16_l2=0.2162, 32_h1=0.8040, 32_l2=0.5550
+    [6] time=10.07, avg_loss=0.2012, train_err=6.2867
+    Eval: 16_h1=0.2600, 16_l2=0.1664, 32_h1=0.7934, 32_l2=0.5296
+    [9] time=9.99, avg_loss=0.2072, train_err=6.4741
+    Eval: 16_h1=0.2641, 16_l2=0.1678, 32_h1=0.7593, 32_l2=0.4923
+    [12] time=10.01, avg_loss=0.2042, train_err=6.3807
+    Eval: 16_h1=0.2544, 16_l2=0.1577, 32_h1=0.7915, 32_l2=0.5030
+    [15] time=9.96, avg_loss=0.1907, train_err=5.9588
+    Eval: 16_h1=0.2567, 16_l2=0.1713, 32_h1=0.7695, 32_l2=0.4561
+    [18] time=10.02, avg_loss=0.1470, train_err=4.5951
+    Eval: 16_h1=0.2394, 16_l2=0.1488, 32_h1=0.7585, 32_l2=0.4966
 
-    {'train_err': 4.346285965293646, 'avg_loss': 0.13908115088939665, 'avg_lasso_loss': None, 'epoch_train_time': 9.86875730099996}
+    {'train_err': 4.85387809574604, 'avg_loss': 0.1553240990638733, 'avg_lasso_loss': None, 'epoch_train_time': 9.995433986999956}
 
 
 
@@ -499,7 +499,7 @@ In practice we would train a Neural Operator on one or multiple GPUs
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (3 minutes 21.485 seconds)
+   **Total running time of the script:** (3 minutes 23.451 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_UNO_darcy.py:

diff --git a/dev/_sources/auto_examples/plot_count_flops.rst.txt b/dev/_sources/auto_examples/plot_count_flops.rst.txt
@@ -80,7 +80,7 @@ This output is organized as a defaultdict object that counts the FLOPS used in e
 
  .. code-block:: none
 
-    defaultdict(<function FlopTensorDispatchMode.__init__.<locals>.<lambda> at 0x7f32ba6a37e0>, {'': defaultdict(<class 'int'>, {'convolution.default': 2982150144, 'bmm.default': 138412032}), 'lifting': defaultdict(<class 'int'>, {'convolution.default': 562036736}), 'lifting.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 25165824}), 'lifting.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 536870912}), 'fno_blocks': defaultdict(<class 'int'>, {'convolution.default': 2147483648, 'bmm.default': 138412032}), 'fno_blocks.fno_skips.0': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.0.conv': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.convs.0': defaultdict(<class 'int'>, {'bmm.default': 34603008}), 'fno_blocks.channel_mlp.0': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.channel_mlp.0.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.channel_mlp.0.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.fno_skips.1': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.1.conv': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.convs.1': defaultdict(<class 'int'>, {'bmm.default': 34603008}), 'fno_blocks.channel_mlp.1': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.channel_mlp.1.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.channel_mlp.1.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.fno_skips.2': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.2.conv': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.convs.2': defaultdict(<class 'int'>, {'bmm.default': 34603008}), 'fno_blocks.channel_mlp.2': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.channel_mlp.2.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.channel_mlp.2.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.fno_skips.3': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.3.conv': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.convs.3': defaultdict(<class 'int'>, {'bmm.default': 34603008}), 'fno_blocks.channel_mlp.3': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.channel_mlp.3.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.channel_mlp.3.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'projection': defaultdict(<class 'int'>, {'convolution.default': 272629760}), 'projection.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'projection.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 4194304})})
+    defaultdict(<function FlopTensorDispatchMode.__init__.<locals>.<lambda> at 0x7fe4e078f7e0>, {'': defaultdict(<class 'int'>, {'convolution.default': 2982150144, 'bmm.default': 138412032}), 'lifting': defaultdict(<class 'int'>, {'convolution.default': 562036736}), 'lifting.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 25165824}), 'lifting.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 536870912}), 'fno_blocks': defaultdict(<class 'int'>, {'convolution.default': 2147483648, 'bmm.default': 138412032}), 'fno_blocks.fno_skips.0': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.0.conv': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.convs.0': defaultdict(<class 'int'>, {'bmm.default': 34603008}), 'fno_blocks.channel_mlp.0': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.channel_mlp.0.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.channel_mlp.0.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.fno_skips.1': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.1.conv': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.convs.1': defaultdict(<class 'int'>, {'bmm.default': 34603008}), 'fno_blocks.channel_mlp.1': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.channel_mlp.1.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.channel_mlp.1.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.fno_skips.2': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.2.conv': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.convs.2': defaultdict(<class 'int'>, {'bmm.default': 34603008}), 'fno_blocks.channel_mlp.2': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.channel_mlp.2.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.channel_mlp.2.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.fno_skips.3': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.3.conv': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.convs.3': defaultdict(<class 'int'>, {'bmm.default': 34603008}), 'fno_blocks.channel_mlp.3': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.channel_mlp.3.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.channel_mlp.3.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'projection': defaultdict(<class 'int'>, {'convolution.default': 272629760}), 'projection.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'projection.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 4194304})})
 
 
 
@@ -125,7 +125,7 @@ To check the maximum FLOPS used during the forward pass, let's create a recursiv
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 3.214 seconds)
+   **Total running time of the script:** (0 minutes 3.202 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_count_flops.py:

diff --git a/dev/_sources/auto_examples/plot_darcy_flow_spectrum.rst.txt b/dev/_sources/auto_examples/plot_darcy_flow_spectrum.rst.txt
@@ -219,7 +219,7 @@ Loading the Navier-Stokes dataset in 128x128 resolution
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.202 seconds)
+   **Total running time of the script:** (0 minutes 0.198 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_darcy_flow_spectrum.py:

diff --git a/dev/_sources/auto_examples/plot_incremental_FNO_darcy.rst.txt b/dev/_sources/auto_examples/plot_incremental_FNO_darcy.rst.txt
@@ -240,15 +240,15 @@ Set up the losses
     )
 
     ### SCHEDULER ###
-     <torch.optim.lr_scheduler.CosineAnnealingLR object at 0x7f32c0126520>
+     <torch.optim.lr_scheduler.CosineAnnealingLR object at 0x7fe4e1aa2520>
 
     ### LOSSES ###
 
     ### INCREMENTAL RESOLUTION + GRADIENT EXPLAINED ###
 
-     * Train: <neuralop.losses.data_losses.H1Loss object at 0x7f32ba62e710>
+     * Train: <neuralop.losses.data_losses.H1Loss object at 0x7fe4e071a710>
 
-     * Test: {'h1': <neuralop.losses.data_losses.H1Loss object at 0x7f32ba62e710>, 'l2': <neuralop.losses.data_losses.LpLoss object at 0x7f32c01269e0>}
+     * Test: {'h1': <neuralop.losses.data_losses.H1Loss object at 0x7fe4e071a710>, 'l2': <neuralop.losses.data_losses.LpLoss object at 0x7fe4e1aa29e0>}
 
 
 
@@ -331,15 +331,15 @@ Train the model
     Raw outputs of shape torch.Size([16, 1, 8, 8])
     [0] time=0.24, avg_loss=0.9239, train_err=13.1989
     Eval: 16_h1=0.8702, 16_l2=0.5536, 32_h1=0.9397, 32_l2=0.5586
-    [1] time=0.23, avg_loss=0.7589, train_err=10.8413
+    [1] time=0.22, avg_loss=0.7589, train_err=10.8413
     Eval: 16_h1=0.7880, 16_l2=0.5167, 32_h1=0.9011, 32_l2=0.5234
     [2] time=0.22, avg_loss=0.6684, train_err=9.5484
     Eval: 16_h1=0.7698, 16_l2=0.4235, 32_h1=0.9175, 32_l2=0.4405
-    [3] time=0.23, avg_loss=0.6082, train_err=8.6886
+    [3] time=0.22, avg_loss=0.6082, train_err=8.6886
     Eval: 16_h1=0.7600, 16_l2=0.4570, 32_h1=1.0126, 32_l2=0.5005
-    [4] time=0.23, avg_loss=0.5604, train_err=8.0054
+    [4] time=0.22, avg_loss=0.5604, train_err=8.0054
     Eval: 16_h1=0.8301, 16_l2=0.4577, 32_h1=1.1722, 32_l2=0.4987
-    [5] time=0.23, avg_loss=0.5366, train_err=7.6663
+    [5] time=0.22, avg_loss=0.5366, train_err=7.6663
     Eval: 16_h1=0.8099, 16_l2=0.4076, 32_h1=1.1414, 32_l2=0.4436
     [6] time=0.23, avg_loss=0.5050, train_err=7.2139
     Eval: 16_h1=0.7204, 16_l2=0.3888, 32_h1=0.9945, 32_l2=0.4285
@@ -354,26 +354,26 @@ Train the model
     Incre Res Update: change res to 16
     [10] time=0.29, avg_loss=0.5158, train_err=7.3681
     Eval: 16_h1=0.5133, 16_l2=0.3167, 32_h1=0.6120, 32_l2=0.3022
-    [11] time=0.28, avg_loss=0.4536, train_err=6.4795
+    [11] time=0.27, avg_loss=0.4536, train_err=6.4795
     Eval: 16_h1=0.4680, 16_l2=0.3422, 32_h1=0.6436, 32_l2=0.3659
     [12] time=0.28, avg_loss=0.4155, train_err=5.9358
     Eval: 16_h1=0.4119, 16_l2=0.2692, 32_h1=0.5285, 32_l2=0.2692
     [13] time=0.28, avg_loss=0.3724, train_err=5.3195
     Eval: 16_h1=0.4045, 16_l2=0.2569, 32_h1=0.5620, 32_l2=0.2747
     [14] time=0.28, avg_loss=0.3555, train_err=5.0783
     Eval: 16_h1=0.3946, 16_l2=0.2485, 32_h1=0.5278, 32_l2=0.2523
-    [15] time=0.29, avg_loss=0.3430, train_err=4.8998
+    [15] time=0.28, avg_loss=0.3430, train_err=4.8998
     Eval: 16_h1=0.3611, 16_l2=0.2323, 32_h1=0.5079, 32_l2=0.2455
     [16] time=0.28, avg_loss=0.3251, train_err=4.6438
     Eval: 16_h1=0.3433, 16_l2=0.2224, 32_h1=0.4757, 32_l2=0.2351
-    [17] time=0.29, avg_loss=0.3072, train_err=4.3888
+    [17] time=0.28, avg_loss=0.3072, train_err=4.3888
     Eval: 16_h1=0.3458, 16_l2=0.2226, 32_h1=0.4776, 32_l2=0.2371
     [18] time=0.28, avg_loss=0.2982, train_err=4.2593
     Eval: 16_h1=0.3251, 16_l2=0.2116, 32_h1=0.4519, 32_l2=0.2245
-    [19] time=0.29, avg_loss=0.2802, train_err=4.0024
+    [19] time=0.28, avg_loss=0.2802, train_err=4.0024
     Eval: 16_h1=0.3201, 16_l2=0.2110, 32_h1=0.4533, 32_l2=0.2245
 
-    {'train_err': 4.002395851271493, 'avg_loss': 0.2801677095890045, 'avg_lasso_loss': None, 'epoch_train_time': 0.2853402149999056, '16_h1': tensor(0.3201), '16_l2': tensor(0.2110), '32_h1': tensor(0.4533), '32_l2': tensor(0.2245)}
+    {'train_err': 4.002395851271493, 'avg_loss': 0.2801677095890045, 'avg_lasso_loss': None, 'epoch_train_time': 0.281409022000048, '16_h1': tensor(0.3201), '16_l2': tensor(0.2110), '32_h1': tensor(0.4533), '32_l2': tensor(0.2245)}
 
 
 
@@ -447,7 +447,7 @@ In practice we would train a Neural Operator on one or multiple GPUs
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 7.050 seconds)
+   **Total running time of the script:** (0 minutes 6.935 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_incremental_FNO_darcy.py: