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neuraloperator · Nov 14, 2024 · 6aa2f70 · 6aa2f70
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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 0x7f0e461dc7f0>
+    <matplotlib.colorbar.Colorbar object at 0x7f8cc01feb50>
 
 
 
@@ -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 36.353 seconds)
+   **Total running time of the script:** (0 minutes 36.596 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 0x7f0e776c2fa0>
+     <torch.optim.lr_scheduler.CosineAnnealingLR object at 0x7f8cf16b82b0>
 
     ### LOSSES ###
 
-     * Train: <neuralop.losses.data_losses.H1Loss object at 0x7f0e776c2f40>
+     * Train: <neuralop.losses.data_losses.H1Loss object at 0x7f8cf16b8820>
 
-     * Test: {'h1': <neuralop.losses.data_losses.H1Loss object at 0x7f0e776c2f40>, 'l2': <neuralop.losses.data_losses.LpLoss object at 0x7f0e7769fbe0>}
+     * Test: {'h1': <neuralop.losses.data_losses.H1Loss object at 0x7f8cf16b8820>, 'l2': <neuralop.losses.data_losses.LpLoss object at 0x7f8cd0cf9940>}
 
 
 
@@ -317,16 +317,16 @@ Then train the model on our small Darcy-Flow dataset:
     Eval: 16_h1=0.2058, 16_l2=0.1716, 32_h1=0.3869, 32_l2=0.2037
     [6] time=2.54, avg_loss=0.1687, train_err=5.2734
     Eval: 16_h1=0.1864, 16_l2=0.1414, 32_h1=0.3874, 32_l2=0.1798
-    [9] time=2.55, avg_loss=0.1457, train_err=4.5546
+    [9] time=2.54, avg_loss=0.1457, train_err=4.5546
     Eval: 16_h1=0.1864, 16_l2=0.1451, 32_h1=0.4279, 32_l2=0.1923
-    [12] time=2.55, avg_loss=0.1348, train_err=4.2138
+    [12] time=2.53, avg_loss=0.1348, train_err=4.2138
     Eval: 16_h1=0.1892, 16_l2=0.1436, 32_h1=0.4446, 32_l2=0.1909
-    [15] time=2.55, avg_loss=0.1176, train_err=3.6743
+    [15] time=2.54, avg_loss=0.1176, train_err=3.6743
     Eval: 16_h1=0.1565, 16_l2=0.1118, 32_h1=0.3807, 32_l2=0.1519
     [18] time=2.54, avg_loss=0.0866, train_err=2.7047
     Eval: 16_h1=0.1576, 16_l2=0.1159, 32_h1=0.4055, 32_l2=0.1698
 
-    {'train_err': 2.8488178942352533, 'avg_loss': 0.0911621726155281, 'avg_lasso_loss': None, 'epoch_train_time': 2.5436050599998907}
+    {'train_err': 2.8488178942352533, 'avg_loss': 0.0911621726155281, 'avg_lasso_loss': None, 'epoch_train_time': 2.536003271000027}
 
 
 
@@ -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 52.263 seconds)
+   **Total running time of the script:** (0 minutes 52.108 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
@@ -239,13 +239,13 @@ Creating the losses
     )
 
     ### SCHEDULER ###
-     <torch.optim.lr_scheduler.CosineAnnealingLR object at 0x7f0e61677be0>
+     <torch.optim.lr_scheduler.CosineAnnealingLR object at 0x7f8cccc411f0>
 
     ### LOSSES ###
 
-     * Train: <neuralop.losses.data_losses.LpLoss object at 0x7f0e6169a6d0>
+     * Train: <neuralop.losses.data_losses.LpLoss object at 0x7f8cccc41ac0>
 
-     * Test: {'l2': <neuralop.losses.data_losses.LpLoss object at 0x7f0e6169a6d0>}
+     * Test: {'l2': <neuralop.losses.data_losses.LpLoss object at 0x7f8cccc41ac0>}
 
 
 
@@ -302,22 +302,22 @@ 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=3.51, avg_loss=2.5811, train_err=10.3245
-    Eval: (32, 64)_l2=1.7888, (64, 128)_l2=2.3886
-    [3] time=3.49, avg_loss=0.4420, train_err=1.7682
-    Eval: (32, 64)_l2=0.4180, (64, 128)_l2=2.5558
-    [6] time=3.46, avg_loss=0.2738, train_err=1.0950
-    Eval: (32, 64)_l2=0.3758, (64, 128)_l2=2.4626
-    [9] time=3.47, avg_loss=0.2376, train_err=0.9506
-    Eval: (32, 64)_l2=0.2817, (64, 128)_l2=2.4057
-    [12] time=3.50, avg_loss=0.2004, train_err=0.8015
-    Eval: (32, 64)_l2=0.2895, (64, 128)_l2=2.3442
-    [15] time=3.46, avg_loss=0.1736, train_err=0.6945
-    Eval: (32, 64)_l2=0.2295, (64, 128)_l2=2.3143
-    [18] time=3.46, avg_loss=0.1513, train_err=0.6052
-    Eval: (32, 64)_l2=0.2300, (64, 128)_l2=2.2962
+    [0] time=3.62, avg_loss=2.5873, train_err=10.3491
+    Eval: (32, 64)_l2=1.6625, (64, 128)_l2=2.3805
+    [3] time=3.59, avg_loss=0.4299, train_err=1.7195
+    Eval: (32, 64)_l2=0.3705, (64, 128)_l2=2.3807
+    [6] time=3.56, avg_loss=0.2634, train_err=1.0535
+    Eval: (32, 64)_l2=0.3257, (64, 128)_l2=2.3691
+    [9] time=3.56, avg_loss=0.2119, train_err=0.8477
+    Eval: (32, 64)_l2=0.2609, (64, 128)_l2=2.3360
+    [12] time=3.55, avg_loss=0.1822, train_err=0.7286
+    Eval: (32, 64)_l2=0.2307, (64, 128)_l2=2.3128
+    [15] time=3.56, avg_loss=0.1581, train_err=0.6322
+    Eval: (32, 64)_l2=0.1898, (64, 128)_l2=2.3094
+    [18] time=3.53, avg_loss=0.1431, train_err=0.5725
+    Eval: (32, 64)_l2=0.1784, (64, 128)_l2=2.3245
 
-    {'train_err': 0.601245778799057, 'avg_loss': 0.15031144469976426, 'avg_lasso_loss': None, 'epoch_train_time': 3.4630227890000356}
+    {'train_err': 0.5559453934431076, 'avg_loss': 0.1389863483607769, 'avg_lasso_loss': None, 'epoch_train_time': 3.525268703999984}
 
 
 
@@ -388,7 +388,7 @@ 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 25.195 seconds)
+   **Total running time of the script:** (1 minutes 27.116 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 0x7f0e616613a0>
+     <torch.optim.lr_scheduler.CosineAnnealingLR object at 0x7f8cccbf1640>
 
     ### LOSSES ###
 
-     * Train: <neuralop.losses.data_losses.H1Loss object at 0x7f0e616611f0>
+     * Train: <neuralop.losses.data_losses.H1Loss object at 0x7f8cccbf1610>
 
-     * Test: {'h1': <neuralop.losses.data_losses.H1Loss object at 0x7f0e616611f0>, 'l2': <neuralop.losses.data_losses.LpLoss object at 0x7f0e616612b0>}
+     * Test: {'h1': <neuralop.losses.data_losses.H1Loss object at 0x7f8cccbf1610>, 'l2': <neuralop.losses.data_losses.LpLoss object at 0x7f8cccbf12e0>}
 
 
 
@@ -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=10.17, avg_loss=0.6441, train_err=20.1282
-    Eval: 16_h1=0.3095, 16_l2=0.2449, 32_h1=0.7229, 32_l2=0.5358
-    [3] time=10.14, avg_loss=0.2428, train_err=7.5868
-    Eval: 16_h1=0.2699, 16_l2=0.2214, 32_h1=0.7097, 32_l2=0.5759
-    [6] time=10.13, avg_loss=0.2352, train_err=7.3495
-    Eval: 16_h1=0.2115, 16_l2=0.1576, 32_h1=0.7179, 32_l2=0.5758
-    [9] time=10.13, avg_loss=0.2030, train_err=6.3450
-    Eval: 16_h1=0.2484, 16_l2=0.1970, 32_h1=0.6780, 32_l2=0.5503
-    [12] time=10.10, avg_loss=0.1893, train_err=5.9155
-    Eval: 16_h1=0.2512, 16_l2=0.1867, 32_h1=0.6899, 32_l2=0.5352
-    [15] time=10.07, avg_loss=0.1575, train_err=4.9215
-    Eval: 16_h1=0.1997, 16_l2=0.1529, 32_h1=0.6739, 32_l2=0.5376
-    [18] time=10.10, avg_loss=0.1334, train_err=4.1696
-    Eval: 16_h1=0.1973, 16_l2=0.1490, 32_h1=0.6727, 32_l2=0.5054
+    [0] time=10.16, avg_loss=0.6551, train_err=20.4733
+    Eval: 16_h1=0.3185, 16_l2=0.2467, 32_h1=0.8165, 32_l2=0.6623
+    [3] time=10.08, avg_loss=0.2450, train_err=7.6547
+    Eval: 16_h1=0.2639, 16_l2=0.2082, 32_h1=0.7555, 32_l2=0.6193
+    [6] time=10.11, avg_loss=0.2239, train_err=6.9981
+    Eval: 16_h1=0.2054, 16_l2=0.1505, 32_h1=0.7867, 32_l2=0.6405
+    [9] time=10.19, avg_loss=0.2389, train_err=7.4641
+    Eval: 16_h1=0.2084, 16_l2=0.1527, 32_h1=0.7668, 32_l2=0.6469
+    [12] time=10.10, avg_loss=0.1708, train_err=5.3378
+    Eval: 16_h1=0.2286, 16_l2=0.1798, 32_h1=0.7274, 32_l2=0.5666
+    [15] time=10.11, avg_loss=0.1493, train_err=4.6665
+    Eval: 16_h1=0.2102, 16_l2=0.1550, 32_h1=0.7123, 32_l2=0.5348
+    [18] time=10.16, avg_loss=0.1667, train_err=5.2080
+    Eval: 16_h1=0.1794, 16_l2=0.1285, 32_h1=0.6974, 32_l2=0.5106
 
-    {'train_err': 4.134030694141984, 'avg_loss': 0.13228898221254348, 'avg_lasso_loss': None, 'epoch_train_time': 10.076509817999977}
+    {'train_err': 4.332731641829014, 'avg_loss': 0.13864741253852844, 'avg_lasso_loss': None, 'epoch_train_time': 10.153018183000086}
 
 
 
@@ -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 25.421 seconds)
+   **Total running time of the script:** (3 minutes 26.027 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 0x7f0e7769c310>, {'': 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 0x7f8cf1695310>, {'': 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 3.686 seconds)
+   **Total running time of the script:** (0 minutes 3.795 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_count_flops.py:

diff --git a/dev/_sources/auto_examples/plot_darcy_flow.rst.txt b/dev/_sources/auto_examples/plot_darcy_flow.rst.txt
@@ -163,7 +163,7 @@ Visualizing the data
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.399 seconds)
+   **Total running time of the script:** (0 minutes 0.406 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_darcy_flow.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 0.276 seconds)
+   **Total running time of the script:** (0 minutes 0.278 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 0x7f0e6277eac0>
+     <torch.optim.lr_scheduler.CosineAnnealingLR object at 0x7f8cdc613910>
 
     ### LOSSES ###
 
     ### INCREMENTAL RESOLUTION + GRADIENT EXPLAINED ###
 
-     * Train: <neuralop.losses.data_losses.H1Loss object at 0x7f0e776900d0>
+     * Train: <neuralop.losses.data_losses.H1Loss object at 0x7f8cc39b83a0>
 
-     * Test: {'h1': <neuralop.losses.data_losses.H1Loss object at 0x7f0e776900d0>, 'l2': <neuralop.losses.data_losses.LpLoss object at 0x7f0e77690a00>}
+     * Test: {'h1': <neuralop.losses.data_losses.H1Loss object at 0x7f8cc39b83a0>, 'l2': <neuralop.losses.data_losses.LpLoss object at 0x7f8cc39b80d0>}
 
 
 
@@ -329,13 +329,13 @@ Train the model
     Training on 100 samples
     Testing on [50, 50] samples         on resolutions [16, 32].
     Raw outputs of shape torch.Size([16, 1, 8, 8])
-    [0] time=0.24, avg_loss=0.8115, train_err=11.5929
+    [0] time=0.25, avg_loss=0.8115, train_err=11.5929
     Eval: 16_h1=0.7332, 16_l2=0.5739, 32_h1=0.7863, 32_l2=0.5720
-    [1] time=0.23, avg_loss=0.6661, train_err=9.5159
+    [1] time=0.22, avg_loss=0.6661, train_err=9.5159
     Eval: 16_h1=0.8889, 16_l2=0.7005, 32_h1=1.1195, 32_l2=0.7407
     [2] time=0.22, avg_loss=0.6497, train_err=9.2815
     Eval: 16_h1=0.6372, 16_l2=0.4883, 32_h1=0.6967, 32_l2=0.5000
-    [3] time=0.22, avg_loss=0.5559, train_err=7.9411
+    [3] time=0.23, avg_loss=0.5559, train_err=7.9411
     Eval: 16_h1=0.6112, 16_l2=0.4348, 32_h1=0.7432, 32_l2=0.4530
     [4] time=0.22, avg_loss=0.4852, train_err=6.9312
     Eval: 16_h1=0.5762, 16_l2=0.4037, 32_h1=0.7138, 32_l2=0.4262
@@ -352,28 +352,28 @@ Train the model
     Incre Res Update: change index to 1
     Incre Res Update: change sub to 1
     Incre Res Update: change res to 16
-    [10] time=0.28, avg_loss=0.4253, train_err=6.0757
+    [10] time=0.29, avg_loss=0.4253, train_err=6.0757
     Eval: 16_h1=0.4069, 16_l2=0.2959, 32_h1=0.4904, 32_l2=0.2928
-    [11] time=0.27, avg_loss=0.3745, train_err=5.3500
+    [11] time=0.28, avg_loss=0.3745, train_err=5.3500
     Eval: 16_h1=0.3820, 16_l2=0.2869, 32_h1=0.4769, 32_l2=0.3026
     [12] time=0.28, avg_loss=0.3405, train_err=4.8636
     Eval: 16_h1=0.3404, 16_l2=0.2598, 32_h1=0.4410, 32_l2=0.2731
     [13] time=0.28, avg_loss=0.3090, train_err=4.4136
     Eval: 16_h1=0.3231, 16_l2=0.2452, 32_h1=0.4245, 32_l2=0.2586
     [14] time=0.28, avg_loss=0.2896, train_err=4.1368
     Eval: 16_h1=0.3130, 16_l2=0.2380, 32_h1=0.4161, 32_l2=0.2522
-    [15] time=0.28, avg_loss=0.2789, train_err=3.9843
+    [15] time=0.29, avg_loss=0.2789, train_err=3.9843
     Eval: 16_h1=0.3072, 16_l2=0.2324, 32_h1=0.4151, 32_l2=0.2455
     [16] time=0.28, avg_loss=0.2690, train_err=3.8434
     Eval: 16_h1=0.3042, 16_l2=0.2305, 32_h1=0.4100, 32_l2=0.2425
     [17] time=0.28, avg_loss=0.2637, train_err=3.7674
     Eval: 16_h1=0.2954, 16_l2=0.2229, 32_h1=0.4023, 32_l2=0.2354
-    [18] time=0.28, avg_loss=0.2557, train_err=3.6533
+    [18] time=0.29, avg_loss=0.2557, train_err=3.6533
     Eval: 16_h1=0.2756, 16_l2=0.2105, 32_h1=0.3780, 32_l2=0.2269
     [19] time=0.28, avg_loss=0.2395, train_err=3.4208
     Eval: 16_h1=0.2735, 16_l2=0.2106, 32_h1=0.3738, 32_l2=0.2303
 
-    {'train_err': 3.4207682268960133, 'avg_loss': 0.23945377588272096, 'avg_lasso_loss': None, 'epoch_train_time': 0.2815485109999827, '16_h1': tensor(0.2735), '16_l2': tensor(0.2106), '32_h1': tensor(0.3738), '32_l2': tensor(0.2303)}
+    {'train_err': 3.4207682268960133, 'avg_loss': 0.23945377588272096, 'avg_lasso_loss': None, 'epoch_train_time': 0.2821420150000904, '16_h1': tensor(0.2735), '16_l2': tensor(0.2106), '32_h1': tensor(0.3738), '32_l2': tensor(0.2303)}
 
 
 
@@ -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 7.019 seconds)
+   **Total running time of the script:** (0 minutes 7.106 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_incremental_FNO_darcy.py: