diff --git a/Tutorials/1. Geometry/Basic_domains.html b/Tutorials/1. Geometry/Basic_domains.html index b237721..ffafa48 100644 --- a/Tutorials/1. Geometry/Basic_domains.html +++ b/Tutorials/1. Geometry/Basic_domains.html @@ -214,7 +214,11 @@ +

2D heat conduction

+ diff --git a/Tutorials/1. Geometry/UKAEA SUT.html b/Tutorials/1. Geometry/UKAEA SUT.html index 5d593a7..5120395 100644 --- a/Tutorials/1. Geometry/UKAEA SUT.html +++ b/Tutorials/1. Geometry/UKAEA SUT.html @@ -214,7 +214,11 @@ +

2D heat conduction

+ @@ -462,7 +466,7 @@

UKAEA SUT -../../_images/32403780c9e54ac8e025643cb84ff86b1a61e80c7b12e57e6e2005dfc8df5616.png +../../_images/e4f0d751caa1199f8da7221078efc2e9554fcb8eeaf64d14f510b9940f6665c7.png
@@ -493,7 +497,7 @@

UKAEA SUT
(-0.1, 1.0)

-../../_images/5ba651f47cc0e1daf3e2be179d1b5be91cb40885c0a25b18d14e9b1fa5513098.png +../../_images/0cc15baf8ec960bc6f1fa767669c82aca0149ba35da22dece2f616afddc753ff.png

The last point of this tutorial is the possibility to transform a TrimeshPolyhedron to a ShapelyPolygon, by either projecting or slicing with a plane. This is also a functionality implemented in Trimesh.

@@ -511,7 +515,7 @@

UKAEA SUT -../../_images/7f934a02c8968aef054e3b04022a627b69b2b806668fcc0cbf3fd7592602623f.png +../../_images/1d8be8ed03de359eb50e5f89d3f57b0a2014f5d9cded4e66d40c869ad20beb0b.png
@@ -527,7 +531,7 @@

UKAEA SUT -../../_images/b543061ae01454ea015b0ffc45b3d680ef2108d14dd845b1f7468c0779c7b173.png +../../_images/148e52340e86d09c22942aa4d96d53570b5767051b883431a9de8f4c09a551bc.png

@@ -543,7 +547,7 @@

UKAEA SUT -../../_images/f60b3402d6ff38d6c2b9ea1d5d04042d576e064fba211ffe14ab55e921d03106.png +../../_images/43ccff2a5b0f4ca65b4dcfa0a2bbf45148a6e0f00ed77a76f78b47c3b7c176e8.png

@@ -559,7 +563,7 @@

UKAEA SUT -../../_images/ce3734c4f6ca0b9dd42f0c077b612fdcd492f9b0b9196891808d017f8dca1ed5.png +../../_images/5e3cb04b902f5983d1da76a6099f3de6fe9c4d5a5118e5d7c586c009aa7c623d.png

diff --git a/Tutorials/1. Geometry/different sampling.html b/Tutorials/1. Geometry/different sampling.html index 1206de4..526c6d1 100644 --- a/Tutorials/1. Geometry/different sampling.html +++ b/Tutorials/1. Geometry/different sampling.html @@ -214,7 +214,11 @@ +

2D heat conduction

+ @@ -453,7 +457,7 @@

Basic sampling techniques -../../_images/c0c963096e7b293346cec67b067178ec3b3d03aa7c52c3efde6d383f83108e22.png +../../_images/4412b36c0dc39a4d6c65c9a6aad08baf8faca887dd58b21a17dd46efa1037249.png ../../_images/f6bd08357ce4e93b2b7f2147c0be430e9f2b5daae716d285580075283b2f422a.png @@ -492,9 +496,9 @@

Basic sampling techniques +../../_images/206b4796802fdd775aa1b3b9a1d4f364bd3ac3d218cd5e3a45de4d94a88230a8.png ../../_images/6fd2ace32477f771af6886a485c069316d1f5aa1d2c7b208781f2fec3da72a49.png -../../_images/7e5af8912d3d18a5d3946cab03f8b6d1d15f31934e8485c5c2257eebc3ccd8cf.png +../../_images/803c7c4e4fd945e1279b081367d874871218657940e31a56b970addb49aa8760.png
@@ -515,7 +519,7 @@

Basic sampling techniques -../../_images/82f3e0ec3eed124da24e2122a668e21c4986e850d8078b6736bca15bc6b587ea.png +../../_images/84ae33e8b02ff510bc2d03ff33d33dfcefbd219ff714f3c1c291e5ef2c9e975b.png

@@ -543,7 +547,7 @@

Basic sampling techniques -../../_images/c5dc96199f60ceb183007948027bc48dee39a061f9e1bc5a8164d669af0068bc.png +../../_images/c07b835172869788e07467f333d4db96b538e6043a51f2a5a69f832c9d487052.png

@@ -564,7 +568,7 @@

Basic sampling techniques -../../_images/c3062e446ec8c314bf0eb2b8f65d6cbe6e216af143b02ce566a56c08f98a6072.png +../../_images/96430ec0f6f71b45aee44a527faf89e202a3b915666556814e270c64f373b79f.png

diff --git a/Tutorials/1. Geometry/domain_creation.html b/Tutorials/1. Geometry/domain_creation.html index fb79c36..232a9e3 100644 --- a/Tutorials/1. Geometry/domain_creation.html +++ b/Tutorials/1. Geometry/domain_creation.html @@ -216,7 +216,11 @@ +

2D heat conduction

+ @@ -570,10 +574,6 @@

Domain Operations -../../_images/601ea68e24e42916aa6a7b94030fdf2a4b96fbbd2f71c4fd41fd65e22f15e63b.png -../../_images/c4b10e252b4a42b63b7f8ba3ffe9dcf0a37f09dfb9b5015ffea44f3b92154f09.png +../../_images/ae1d790f90877cf596b916473acbdce58b4c552893ed07782878212cc3a267a6.png +../../_images/0b65fb81935d9b536bac49b9ce552789a856696698c60790203554ddf2b235dc.png

For the product, we create the Cartesian product of an interval and circle to get a cylinder:

diff --git a/Tutorials/1. Geometry/polygons_external_objects.html b/Tutorials/1. Geometry/polygons_external_objects.html index 9368407..e4d7034 100644 --- a/Tutorials/1. Geometry/polygons_external_objects.html +++ b/Tutorials/1. Geometry/polygons_external_objects.html @@ -214,7 +214,11 @@ +

2D heat conduction

+ @@ -518,7 +522,7 @@

Polygons
-../../_images/9d910c59b979351580bad7b9326f56df23f0daf068ee071783c357b1d4e68195.png +../../_images/91f6b7848fce08ea52b2001f2a0cef281609e98f5ca01895fd8a98558480308d.png
@@ -547,7 +551,7 @@

External Objects -../../_images/92f74df55b4a0f1b62f1025f5f1fe8b6dcff79a2f3bd957874940b437dce8415.png +../../_images/47b4b341773de9f6d693a5307d953a244c698eb425b184360da2fea718480472.png

The last point of this tutorial is the possibility to transform a TrimeshPolyhedron to a ShapelyPolygon, by either projecting or slicing with a plane. This is also a functionality implemented in Trimesh.

@@ -571,7 +575,7 @@

External Objects -../../_images/44c5413b0f3a3c74d2a24014a43e6b1d8de981c8ba61240fdcfbe2c96aaf1160.png +../../_images/7ebbc278b003591cf284a4aa06811025a7863bf2774eded0b519e12e91dabae8.png diff --git a/Tutorials/2. BC/1. dirichlet.html b/Tutorials/2. BC/1. dirichlet.html index 4e7b3df..20995f5 100644 --- a/Tutorials/2. BC/1. dirichlet.html +++ b/Tutorials/2. BC/1. dirichlet.html @@ -214,7 +214,11 @@ +

2D heat conduction

+ @@ -457,7 +461,7 @@

Dirichlet BC -../../_images/0186444e05b9dbd7cd354de3bcf15027856b9cb5a4d15061b390427ee5d46537.png +../../_images/356c89c69072e030e0287e4c465db13fa52c43d32cb30d4f4149bf7bad9f3df0.png
@@ -485,8 +489,8 @@

Dirichlet BC -../../_images/83cc70abb48ed5a2b3af6fe9453ebaee5d052294aba593a10cbbfad533968808.png -../../_images/6bb4d4272074ecea8f6d362c8a1d494d39388e06a1344129e6d633b33381f7f8.png +../../_images/f3154b9d6fa20e9f871e5b99434545ceea5c83645e93913d3afd6ef02c065ad6.png +../../_images/e180e1ed38a4822a6dc32cc96ecd7c16345d56becfa5b569ab0c15a79699fd13.png

@@ -560,10 +564,10 @@

Dirichlet BC -
<matplotlib.colorbar.Colorbar at 0x7fe83c7ed3a0>
+
<matplotlib.colorbar.Colorbar at 0x7889653af640>
 

 

-../../_images/424fb68800caad5fb747328d71d1e6472dc0876b7156b2fd4893c035e19bd8f3.png
+../../_images/839a69eaf26308291b53592f0b0d9327e280be616163644c64c5fa69326408ee.png
@@ -575,10 +579,10 @@

Dirichlet BC -
<matplotlib.colorbar.Colorbar at 0x7fe83c6df730>
+
<matplotlib.colorbar.Colorbar at 0x78896527d610>
 

 

-../../_images/dc7ff1d539022e6f2365e63b905248026678bb871d438f706ad0fec1f9ebbb57.png
+../../_images/4325d5ebda78f8b4c9ad0056a8435f6abd09749ebbee6487f91c08698403c931.png
diff --git a/Tutorials/2. BC/2. pde.html b/Tutorials/2. BC/2. pde.html index d74b787..bb55dca 100644 --- a/Tutorials/2. BC/2. pde.html +++ b/Tutorials/2. BC/2. pde.html @@ -214,7 +214,11 @@ +

2D heat conduction

+

@@ -455,7 +459,7 @@

PDE constraint -../../_images/670d6533cb731d7f0c6998a7992010e2677583a658b49298bb69b86ee563a900.png +../../_images/6b62d3512589461300084f9fc0dcd43b24785ddb82c60bbea66fd173664982b1.png

@@ -510,10 +514,10 @@

PDE constraint -
<matplotlib.colorbar.Colorbar at 0x7ff085fec070>
+
<matplotlib.colorbar.Colorbar at 0x7494db6c2790>
 

 

-../../_images/1c6b5d55d73ceb37ba2f0df6e64712cd849709e39e2d9b53fc10a5cc1b6c2b0b.png
+../../_images/2d1f5ea337879b06b699b47c506005ee7bda8ec804903bab98d9fd46b0fd8f5c.png
diff --git a/Tutorials/3. Gradients/1. Gradients.html b/Tutorials/3. Gradients/1. Gradients.html index e0df5f2..90a3259 100644 --- a/Tutorials/3. Gradients/1. Gradients.html +++ b/Tutorials/3. Gradients/1. Gradients.html @@ -216,7 +216,11 @@ +

2D heat conduction

+

@@ -589,7 +593,7 @@

1D tensor with multiple values. -
tensor([1.4514], grad_fn=<AddBackward0>)
+
tensor([1.5027], grad_fn=<AddBackward0>)
@@ -604,7 +608,7 @@

1D tensor with multiple values. -
Gradient (dy/dx): tensor([0.3535, 0.6050])
+
Gradient (dy/dx): tensor([0.6726, 0.4256])
@@ -617,7 +621,7 @@

1D tensor with multiple values. -
tensor([0.3535, 0.6050], grad_fn=<SqueezeBackward1>)
+
tensor([0.6726, 0.4256], grad_fn=<SqueezeBackward1>)
@@ -641,8 +645,8 @@

1D tensor with multiple values. -
(tensor([1.4514], grad_fn=<AddBackward0>),
- tensor([1.4514], grad_fn=<AddBackward0>))
+
(tensor([1.5027], grad_fn=<AddBackward0>),
+ tensor([1.5027], grad_fn=<AddBackward0>))
@@ -701,10 +705,7 @@

2D tensor

-
Gradient (dy/dx): 
-
-
-
tensor([[ 0.5403, -0.4161],
+
Gradient (dy/dx): tensor([[ 0.5403, -0.4161],
         [-0.9900, -0.6536]])
 

 

@@ -755,7 +756,7 @@ 
Gradients with actual geometry
-../../_images/8c5fc112fc2230ead56f14dc9dce986259fe7dc8305bf1320a2013b3be4cde6c.png
+../../_images/d1275a39f2f5341d08d4b7fe552a0827170392a069a4094fd5602ed73b3e435b.png
@@ -810,10 +811,10 @@

Gradients with actual geometry -
<matplotlib.colorbar.Colorbar at 0x7f750007aa90>
+
<matplotlib.colorbar.Colorbar at 0x7b9c52997580>
 

 

-../../_images/17c4a27f795d2a51f4bbbbb0a659c37d531673549fd333f1332e89ff49588845.png
+../../_images/ec6ca55da4612b122ba57444d7f5d1ef35817e47b7d983ce1ba4ba1c4a9dfca4.png
@@ -842,106 +843,106 @@

Gradients with actual geometry -
Gradient (dy/dx): tensor([[0.2930, 0.0000],
-        [1.0825, 0.0000],
-        [0.5305, 0.0000],
-        [1.9212, 0.0000],
-        [0.6438, 0.0000],
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-        [0.9573, 0.0000],
-        [1.0166, 0.0000],
-        [0.8906, 0.0000],
-        [1.1743, 0.0000],
-        [0.9489, 0.0000],
-        [0.0287, 0.0000],
+
Gradient (dy/dx): tensor([[0.6733, 0.0000],
+        [0.6685, 0.0000],
+        [1.0176, 0.0000],
+        [1.6641, 0.0000],
+        [1.9967, 0.0000],
+        [0.6386, 0.0000],
+        [1.2306, 0.0000],
+        [1.7671, 0.0000],
+        [1.3454, 0.0000],
+        [1.8883, 0.0000],
+        [1.2225, 0.0000],
+        [0.8830, 0.0000],
+        [1.5180, 0.0000],
+        [1.6009, 0.0000],
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+        [1.4308, 0.0000],
+        [1.0261, 0.0000],
+        [1.5584, 0.0000],
+        [1.1730, 0.0000],
+        [0.7687, 0.0000],
+        [0.8867, 0.0000],
+        [1.4550, 0.0000],
+        [0.8041, 0.0000],
+        [1.5583, 0.0000],
+        [1.5580, 0.0000],
+        [1.7970, 0.0000],
+        [0.5377, 0.0000],
+        [0.6538, 0.0000],
+        [1.7832, 0.0000],
+        [0.4909, 0.0000],
+        [0.8772, 0.0000],
+        [0.5479, 0.0000],
+        [0.1592, 0.0000],
+        [0.0740, 0.0000],
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+        [0.7644, 0.0000],
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@@ -962,106 +963,106 @@

Gradients with actual geometry -
tensor([[0.2930],
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+
tensor([[0.6733],
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-        [0.5493],
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-        [1.3253],
-        [0.7525],
-        [1.6583],
-        [0.8198],
-        [1.6443],
-        [1.0415],
-        [0.1759],
-        [0.8441],
-        [1.4261],
-        [0.4104],
-        [1.6782],
-        [1.8920],
-        [0.1505],
-        [1.7075],
-        [0.7404],
-        [0.9070],
-        [0.1176],
-        [0.4098],
-        [1.0612],
-        [1.1286],
-        [0.0742],
-        [0.6482],
-        [0.9419],
-        [0.4051],
-        [0.8317],
-        [0.3231],
-        [1.0585],
-        [0.3959],
-        [1.1475],
-        [0.9752],
-        [1.3819],
-        [0.4927],
-        [0.0505],
-        [0.3465],
-        [1.9483],
-        [1.2960],
-        [1.3112],
-        [1.1125],
-        [0.8214],
-        [0.3367],
-        [1.5440],
-        [0.7444],
-        [1.2778]], grad_fn=<IndexBackward0>)
+        [0.9643],
+        [0.0257],
+        [0.5479],
+        [1.2799],
+        [1.7555],
+        [0.0478],
+        [1.9280],
+        [0.6001],
+        [0.9786],
+        [0.3538],
+        [1.6759],
+        [0.8473],
+        [1.0966],
+        [0.3954],
+        [0.9916],
+        [0.3232],
+        [1.4093],
+        [1.1539],
+        [1.3791],
+        [1.8612],
+        [1.5549],
+        [1.1919],
+        [0.9863],
+        [1.0187],
+        [0.9845],
+        [1.5591],
+        [0.8240],
+        [0.3481],
+        [1.8477],
+        [1.0604],
+        [1.3063],
+        [1.8167],
+        [0.5160],
+        [0.2010],
+        [0.6498],
+        [1.6442],
+        [0.4801],
+        [1.3056],
+        [0.1697],
+        [0.2895],
+        [0.9645],
+        [1.3555],
+        [0.8697],
+        [0.7806],
+        [1.7696],
+        [0.2021],
+        [0.8245],
+        [0.3633],
+        [1.2962],
+        [0.0385],
+        [1.0791],
+        [1.5716],
+        [0.1625],
+        [0.2896],
+        [1.5592],
+        [1.9910],
+        [0.7264],
+        [1.9206],
+        [0.6380],
+        [1.8515],
+        [1.3841],
+        [1.5092],
+        [0.7644],
+        [0.4880],
+        [0.4294]], grad_fn=<IndexBackward0>)
diff --git a/Tutorials/3. Gradients/2. higher derivative.html b/Tutorials/3. Gradients/2. higher derivative.html index 1386531..44be4ba 100644 --- a/Tutorials/3. Gradients/2. higher derivative.html +++ b/Tutorials/3. Gradients/2. higher derivative.html @@ -214,7 +214,11 @@ +

2D heat conduction

+
@@ -479,7 +483,7 @@

Gradients in DeepINN 
True
-<IndexBackward0 object at 0x7f6b65325040>
+<IndexBackward0 object at 0x7581944093d0>
 
tensor([[2.]], grad_fn=<IndexBackward0>)
diff --git a/Tutorials/4. Dataset/1. basic.html b/Tutorials/4. Dataset/1. basic.html
index 62b4fa0..2fa0eb2 100644
--- a/Tutorials/4. Dataset/1. basic.html	
+++ b/Tutorials/4. Dataset/1. basic.html	
@@ -214,7 +214,11 @@
 
+
2D heat conduction

+
diff --git a/Tutorials/5. FCNN/1. basic.html b/Tutorials/5. FCNN/1. basic.html index b02c556..a506b3c 100644 --- a/Tutorials/5. FCNN/1. basic.html +++ b/Tutorials/5. FCNN/1. basic.html @@ -214,7 +214,11 @@ +

2D heat conduction

+
diff --git a/Tutorials/5. FCNN/2. test.html b/Tutorials/5. FCNN/2. test.html index d3faee8..f417865 100644 --- a/Tutorials/5. FCNN/2. test.html +++ b/Tutorials/5. FCNN/2. test.html @@ -60,7 +60,7 @@ - + @@ -214,7 +214,11 @@ +

2D heat conduction

+
@@ -481,9 +485,9 @@

Forward pass -
tensor([[-0.0735],
-        [ 0.2387],
-        [-0.4911]])
+
tensor([[-1.0178],
+        [-1.9623],
+        [-0.3584]])
@@ -496,9 +500,9 @@

Forward pass -
tensor([[-0.1239],
-        [-0.1970],
-        [ 0.0000]], grad_fn=<AddmmBackward0>)
+
tensor([[0.0563],
+        [0.0000],
+        [0.0799]], grad_fn=<AddmmBackward0>)
@@ -510,9 +514,9 @@

Forward pass -
tensor([[-0.0734],
-        [ 0.2342],
-        [-0.4551]])
+
tensor([[-0.7690],
+        [-0.9613],
+        [-0.3438]])
@@ -537,7 +541,7 @@

Forward pass -
tensor(0.1445, grad_fn=<MseLossBackward0>)
+
tensor(1.7321, grad_fn=<MseLossBackward0>)
@@ -588,7 +592,7 @@

Forward pass

next

-

FCNN training

+

1D Laplace Equation

 diff --git a/Tutorials/5. FCNN/3. model.html b/Tutorials/5. FCNN/3. model.html index 1ab3520..077cd4c 100644 --- a/Tutorials/5. FCNN/3. model.html +++ b/Tutorials/5. FCNN/3. model.html @@ -8,7 +8,7 @@ - FCNN training — DeepINN + 1D Laplace Equation — DeepINN @@ -60,6 +60,7 @@ + @@ -213,7 +214,11 @@ +

2D heat conduction

+

@@ -399,7 +404,7 @@
-

FCNN training

+

1D Laplace Equation

@@ -423,8 +428,8 @@

Contents

-
-

FCNN training#

+
+

1D Laplace Equation#

# This is only valid when the package is not installed
@@ -573,29 +578,29 @@ 
Network#<
-
Iteration: 1 	 BC Loss: 0.1876	 PDE Loss: 0.0000 	 Loss: 0.1876
-Iteration: 51 	 BC Loss: 0.0990	 PDE Loss: 0.0000 	 Loss: 0.0990
+
Iteration: 1 	 BC Loss: 0.0286	 PDE Loss: 0.0000 	 Loss: 0.0286
+Iteration: 51 	 BC Loss: 0.0061	 PDE Loss: 0.0000 	 Loss: 0.0061
 

 

-
Iteration: 101 	 BC Loss: 0.0583	 PDE Loss: 0.0000 	 Loss: 0.0583
-Iteration: 151 	 BC Loss: 0.0352	 PDE Loss: 0.0000 	 Loss: 0.0352
+
Iteration: 101 	 BC Loss: 0.0021	 PDE Loss: 0.0000 	 Loss: 0.0021
+Iteration: 151 	 BC Loss: 0.0004	 PDE Loss: 0.0000 	 Loss: 0.0004
 

 

-
Iteration: 201 	 BC Loss: 0.0205	 PDE Loss: 0.0000 	 Loss: 0.0205
-Iteration: 251 	 BC Loss: 0.0112	 PDE Loss: 0.0000 	 Loss: 0.0112
+
Iteration: 201 	 BC Loss: 0.0001	 PDE Loss: 0.0000 	 Loss: 0.0001
+Iteration: 251 	 BC Loss: 0.0000	 PDE Loss: 0.0000 	 Loss: 0.0000
 

 

-
Iteration: 301 	 BC Loss: 0.0057	 PDE Loss: 0.0000 	 Loss: 0.0057
-Iteration: 351 	 BC Loss: 0.0027	 PDE Loss: 0.0000 	 Loss: 0.0027
+
Iteration: 301 	 BC Loss: 0.0000	 PDE Loss: 0.0000 	 Loss: 0.0000
+Iteration: 351 	 BC Loss: 0.0000	 PDE Loss: 0.0000 	 Loss: 0.0000
 

 

-
Iteration: 401 	 BC Loss: 0.0011	 PDE Loss: 0.0000 	 Loss: 0.0011
-Iteration: 451 	 BC Loss: 0.0004	 PDE Loss: 0.0000 	 Loss: 0.0004
+
Iteration: 401 	 BC Loss: 0.0000	 PDE Loss: 0.0000 	 Loss: 0.0000
 

 

-
Iteration: 501 	 BC Loss: 0.0002	 PDE Loss: 0.0000 	 Loss: 0.0002
+
Iteration: 451 	 BC Loss: 0.0000	 PDE Loss: 0.0000 	 Loss: 0.0000
+Iteration: 501 	 BC Loss: 0.0000	 PDE Loss: 0.0000 	 Loss: 0.0000
 Training finished
-Time taken: 'trainer' in 2.9356 secs
+Time taken: 'trainer' in 3.1389 secs
 

 

 

@@ -658,7 +663,7 @@ 
Network#<
 

 

 

-../../_images/503fdc76aa0d00d0edd83a26bf748346fd99bced3335c1a9ba19fbca0b4e9981.png
+../../_images/19050569b3df551f5f93dd00977ad379ba45e8bd50c1a9168a71cb6e542bca6e.png
 

 

 

@@ -674,7 +679,7 @@ 
Network#<
 
Text(0, 0.5, 'Loss')
 

 

-../../_images/07ac1071c10314a37e1b4519828e061097c795b970420d4421ff7539ebb98be8.png
+../../_images/f43fd397329119ac04d988e9cc8234ef260c1308bcb8b20889a035eafe9b4070.png
@@ -719,6 +724,15 @@

Network#<

Forward pass

+ +
+

next

+

2D Laplace Equation

+
+ +
diff --git a/Tutorials/6. 2D heat conduction/1. model.html b/Tutorials/6. 2D heat conduction/1. model.html index cf811d2..23f42e9 100644 --- a/Tutorials/6. 2D heat conduction/1. model.html +++ b/Tutorials/6. 2D heat conduction/1. model.html @@ -8,7 +8,7 @@ - FCNN training — DeepINN + 2D Laplace Equation — DeepINN @@ -60,7 +60,7 @@ - + @@ -213,7 +213,11 @@ +

2D heat conduction

+
@@ -399,7 +403,7 @@
-

FCNN training

+

2D Laplace Equation

@@ -423,8 +427,8 @@

Contents

-
Iteration: 1 	 BC Loss: 0.8491	 PDE Loss: 14.7276 	 Loss: 15.5767
+
Iteration: 1 	 BC Loss: 2.1201	 PDE Loss: 130.2115 	 Loss: 132.3316
 

 

-
Iteration: 501 	 BC Loss: 0.3197	 PDE Loss: 0.0032 	 Loss: 0.3229
+
Iteration: 501 	 BC Loss: 1.4108	 PDE Loss: 5.5380 	 Loss: 6.9488
 

 

-
Iteration: 1001 	 BC Loss: 0.2541	 PDE Loss: 0.0012 	 Loss: 0.2553
+
Iteration: 1001 	 BC Loss: 0.7701	 PDE Loss: 0.2101 	 Loss: 0.9802
 

 

-
Iteration: 1501 	 BC Loss: 0.2520	 PDE Loss: 0.0010 	 Loss: 0.2530
+
Iteration: 1501 	 BC Loss: 0.5292	 PDE Loss: 0.0038 	 Loss: 0.5330
 

 

-
Iteration: 2001 	 BC Loss: 0.2514	 PDE Loss: 0.0008 	 Loss: 0.2522
+
Iteration: 2001 	 BC Loss: 0.4286	 PDE Loss: 0.0025 	 Loss: 0.4310
 

 

-
Iteration: 2501 	 BC Loss: 0.2508	 PDE Loss: 0.0006 	 Loss: 0.2514
+
Iteration: 2501 	 BC Loss: 0.3487	 PDE Loss: 0.0019 	 Loss: 0.3506
 

 

-
Iteration: 3001 	 BC Loss: 0.2501	 PDE Loss: 0.0005 	 Loss: 0.2507
+
Iteration: 3001 	 BC Loss: 0.2640	 PDE Loss: 0.0014 	 Loss: 0.2654
 

 

-
Iteration: 3501 	 BC Loss: 0.2495	 PDE Loss: 0.0005 	 Loss: 0.2500
+
Iteration: 3501 	 BC Loss: 0.1792	 PDE Loss: 0.0009 	 Loss: 0.1801
 

 

-
Iteration: 4001 	 BC Loss: 0.2489	 PDE Loss: 0.0004 	 Loss: 0.2494
+
Iteration: 4001 	 BC Loss: 0.1062	 PDE Loss: 0.0006 	 Loss: 0.1068
 

 

-
Iteration: 4501 	 BC Loss: 0.2485	 PDE Loss: 0.0004 	 Loss: 0.2489
+
Iteration: 4501 	 BC Loss: 0.0540	 PDE Loss: 0.0004 	 Loss: 0.0544
 

 

-
Iteration: 5001 	 BC Loss: 0.2481	 PDE Loss: 0.0005 	 Loss: 0.2485
+
Iteration: 5001 	 BC Loss: 0.0232	 PDE Loss: 0.0002 	 Loss: 0.0234
 Training finished
-Time taken: 'trainer' in 27.7122 secs
+Time taken: 'trainer' in 27.7988 secs
 

 

 

@@ -667,15 +671,15 @@ 
Network#<
 

 

 
plt.figure(1)
-plt.scatter(coordinates_list[0][:,0], coordinates_list[0][:,1], c=solution_list[0][:,0], label = "collocation points",  cmap=plt.get_cmap('plasma', 10))
-plt.scatter(coordinates_list[1][:,0], coordinates_list[1][:,1], c=solution_list[1], label = "boundary points", cmap=plt.get_cmap('plasma', 10))
+plt.scatter(coordinates_list[0][:,0], coordinates_list[0][:,1], c=solution_list[0][:,0], label = "collocation points",  cmap=plt.get_cmap('plasma', 10), edgecolors='k')
+plt.scatter(coordinates_list[1][:,0], coordinates_list[1][:,1], c=solution_list[1], label = "boundary points", cmap=plt.get_cmap('plasma', 10),edgecolors='k')
 plt.colorbar()
 plt.grid('minor')
 

 

 

 

-../../_images/7fb2d7372ff7095a03a2c6c3887993d785d7fb802670617528a1fd1ac14b3c12.png
+../../_images/39d80410820569dc0331ba86f5c8d2193001d3836020e4748def8b4bb12eff42.png
 

 

 

@@ -691,7 +695,7 @@ 
Network#<
 
Text(0, 0.5, 'Loss')
 

 

-../../_images/84b9d2ef9b39ac9048441be1b0869fc9d735296a1b54c4b8dea12976c6fbdf9e.png
+../../_images/25fddf1fad6af54746955fa162f7f9b0e9c64c6968e53759096fe85fcdc4ccbb.png
 

 

 
@@ -733,7 +737,7 @@ 
Network#<
       
       

         
previous

-        
FCNN training

+        
1D Laplace Equation

       

     
 

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diff --git a/_sources/Tutorials/5. FCNN/3. model.ipynb b/_sources/Tutorials/5. FCNN/3. model.ipynb
index 6ceb093..2e4134b 100644
--- a/_sources/Tutorials/5. FCNN/3. model.ipynb	
+++ b/_sources/Tutorials/5. FCNN/3. model.ipynb	
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# FCNN training"
+    "# 1D Laplace Equation "
    ]
   },
   {
diff --git a/_sources/Tutorials/6. 2D heat conduction/1. model.ipynb b/_sources/Tutorials/6. 2D heat conduction/1. model.ipynb
index 4802121..b109ad2 100644
--- a/_sources/Tutorials/6. 2D heat conduction/1. model.ipynb	
+++ b/_sources/Tutorials/6. 2D heat conduction/1. model.ipynb	
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# FCNN training"
+    "# 2D Laplace Equation"
    ]
   },
   {
@@ -77,7 +77,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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y/uTJk+jp6cHx48fx1ltv4ZlnnpnxOZfLBZfLBZ/Ph8LCQlXbCgQCqt/LC9Ex13b24uyla6KcOm8cJwYHk3Y4q281NEwsyb8pUpRdnDOOLQ/+S7iiswzHhyhojVuX2JSWlqKurg6HDh2C2+1GZWUl2tvb8eGHHwIANm3ahMcff1zPJqQiOgWamFQijWssU4PpqUkgEBBOaAhjMHumU5fYZGVlobW1NbIcrtmE2bdvn56vl47wtO5P5/oxMalE1vNcIyHsg9k9THS5goWEHcR/L184Yz1N3xI8YPZEAfXZMCBR45rsTXsEv5jdw0Riw4BE07fUjk9owYhByuwH7ZHYcIZIPS8EPxgxSJndw2RbseE1XaF2fEILIgxSti0Q83rfFGrHNw+Z75tsxg3Kjca2zobXkYDa8c1D5nqY2fUWI7Ct2FC6Yj94HWC0EKsMwLtw2jaNonTFfrBINcxK3XgtAyTCts6G0hX7wSLVMCt1E9Gl2VZsCH6wamaQxQBjliiIWAawbRpF8IOIKYFazErdRCwDkLMhmCNiShBmLldmVuomYhmAxIZgjogpQZi5ajIiioJZkNgQzBGhRyQeZrsyXjvdtUBiQzDHqNGfxYlptiuTqRGRCsSENLAoNJtdqBW5nhUNORtCGlicmGbXZESuZ0VjK7GRKf+1A8n+XjKdmGFErmdFYyuxkSn/tQPJ/l4ynZhhZJrNspXYyJT/2oFkfy+rT0xyyslhqwKxCPf8IK7B++8lc+ezGdjK2cSy2TQ68QvvaRE55eSwldjEstme3ceojsMpvNcrZCxIm4mQYmOkG6HRidAK786LN4QUGyNnlWh00o7dU1DenRdvCFkgNtKNiHipPi9QgZRIBiGdjZFuhEYn7VAKSiSDLrEZHh5GbW0t0tPTsWbNGni9XgBAU1MT/vjjD/T19aGlpQVOp9OQYMNQrswHyYq+3dMuu6NLbDo6OuDxeOB2u1FeXh4Rm+effx4A8Mknn+DIkSOorKzUH+k0RHQjMp5oyYo+dXDbG11iEwwGUVRUBABITU2d8VooFMKBAwfw9ttvz/qc3++H3+9HV1cXAoGAqm319fWpfi8vTI/54KnzcGIUTgcAhLD306Nwr1pqWSwjY+P4+udejIyNIzM9DSUFeViQPvvnT3Y/77gnB0AOAOBi92+4mOC9BRn9cK6ciCw7MvoN+01FPz5EQmvcusTG6XQiGAyiuLgYk5OTkfWDg4N48skn0dzcDIfDMetzLpcLLpcLPp8PhYWFqrYVCARUv5cXpsdc29mLs5euCfKK69Lwgte6/2eqn+jq/y9dxYnBkZiuwsz9vOO7wYizAYDbVy40bFuiHx8ioTVuXbNRpaWl+Pjjj1FTUwO32x1JlzZt2oT+/n68+uqr+Oabb/RsQhpYt97zUMylmT97o8vZZGVlobW1NbIcrtl0dHToi0pCWBe1eegnErHWJjNW1xGFnPoWkblONLN/eNZiR/CH1QV7EhtOMPuHJ1dBRGN1ak1iwwk81FQIeVDjlK1Orbm/XCH8YPYPfzhr6IPZeYN1AZmQCzWXklhdsOfe2YR3mnPlRGSnyZgOUE2FMBI1Ttnq1Jp7sbFLekE1FSIeWiYPeJh9jIb7NIrSC8LuaLm6nseeJu6dTTi9cGT04/aVC7nYaYR6ZLwmzGq0uHsenTL3zia808pXr8BHNXfSgSoYdM8b/cji7rl3NlZAo6952KXmZiayTB6Q2IBufWAmVhQq/xy6goOnzqO2s1fKwYLHlEgL3KdRVkCjr3lYUaisee8EegZHKVXjHHI24HOaUBasGJV7h8L3Cbq2LCsip/zkbMDnNCGhHlkKqGoQueBOzgbJjb4ijyyysqfyNuz99ChWXJcmdAFVDSKn/CQ2SULFZP64IXs+3KuWGnrnQ14HFZFTfkqjkkTkkYVQD6/pisgpPzmbJBF5ZCHUY8agEu2Wtt+RmfR3iDwNTs4mSUQeWQj1mFF0jnZLX/3cq/s7RYKcTZKIPLIQ6jGjazfaHY2Mjev+TpEgsbEZiQqfsnfiJoMZg0p0Cp4Z47ldMkNplM1IVPikTlxziU7BSwryWIdkKfaSVgPhdWp0LhIVPu3UicuCaLck4tMw9UDORiO8To3ORaLCp506cQnrIbHRiKj9Nolm0/ZU3obFORk00yY54YcI3Nv8jaUPEaA0SiOi9tskKnya0YlL8AerLngSG43IckMjNYhanyJiw8qVk9hoxE79NrJfD2Y3MWXlynXVbIaHh7Fx40Zs2bIF77//fmR9IBCA1+uF1+u1XcVdRkStT6lF1GK/Vlh1wetyNh0dHfB4PHC73SgvL4fX6wUAtLS04I033kBKSgoaGhrw1ltvGRIswQZR61NqkV1Mo2HlynWJTTAYRFFREQAgNTU1sn5gYAALFy4EAAwNDc36nN/vh9/vR1dXl2rn09fXJ5xLkiXm7Xdk4qucEEbGxpGZnoaSgkzu/i89+3rtsnH0OCYiy4tzxi35/0Q8PgDtcesSG6fTiWAwiOLiYkxOTkbW5+bmYmBgACkpKXA4HLM+53K54HK54PP5UFiobuYjEAiofi8vyBTz6lsZBJMEevb1kvybZhT7/+dBa2o2Ih4fgPa4dYlNaWkp6urqcOjQIbjdblRWVqK9vR3btm3D1q1bAQANDQ16NkEQpmOnYj9LdIlNVlYWWltbI8vhmk1hYSHa2tr0RUYQhFRQBzFBEJZAYkMQhCWQ2BAEYQkkNgRBWALTyxW6u7vh8/lUvzc/P9/cgAyGYrYOEeMWMWZAfdzd3d0zVyiC8PTTT7MOIWkoZusQMW4RY1YU7XELk0a5XC7WISQNxWwdIsYtYsyA9rhTFEVRDI6FIAhiFsI4G4IgxIY7sRH1thXx4m5qasLjjz+O9evXIxgMMoxwNvFiBoDTp08jLy8PoVCIUXTxiRf3hQsXUF9fj61bt+L7779nGOFs4sXc2dmJsrIylJWV4YsvvmAYYWx+//13bN68GR6PZ8Z6TeejoZUjA2hra1M+++wzRVEUpaysLLK+urpa+fvvv5X+/n7lscceYxVeXOLFHaajo0Npa2uzOqyExIt5bGxMqa+vV6qqqpShoSFW4cUlXtw+n0958cUXlfr6eqW7u5tVeDGJF/Ozzz6rnDt3Tjl//ryybds2RtHNzfr162csazkfuXM2wWAQy5cvBxD7thW5ubkxb1vBmnhxA0AoFMKBAwewbt06BpHFJ17MO3fuRH19PVJSUliFlpB4cXd1daGqqgo7duzAK6+8wiq8mMSLuaKiAg8//DAeeughPProo6zCSxot5yN3YhO+bQWAmLetGBwcjHnbCtbEi3twcBA1NTVobm7mLu54MZ88eRK7du3C8ePHubzxWby4nU4nFi1ahOzsbIyO8nUDrHgxNzY24ujRo/j222/R1NTEKryk0XI+cjcbNTw8jLq6OmRkZODuu+/G4cOH0d7ejkAggObmZgBTt63g7T4g8eIuLS3F1atXsWzZMpSVleG+++5jHWqEeDGH2bRpE15//XVkZ2czjHI28eI+c+YMmpubkZKSgurqatx1112sQ40QL+b9+/fj8OHDAICSkhJUVVUxjnQmf/31F1566SV8+eWXqK6uxpkzZzSfj9yJDUEQcsJdGkUQhJyQ2BAEYQkkNgRBWAKJDWEpbW1tuPfee/H555+zDoWwGHoiJmE4n3zyCc6dO4dbbrkFx48fx4svvhh5raqqasbUL2EfSGwIw3nooYdQV1eH7777Dh988AHrcAhOoDSKMBxFUdDf34958+ZhfHycdTgEJ5CzIQynpaUFjzzyCJYsWYKXX3450vwFAJ9//jneffddLFiwANnZ2VizZg27QAlLoaY+giAsgZwNYSpNTU2R65RuvvlmVFRUMI6IYAU5G4IgLIEKxARBWAKJDUEQlkBiQxCEJfwf6lYxB7KcHVIAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "
"
       ]
@@ -87,7 +87,7 @@
     },
     {
      "data": {
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",
       "text/plain": [
        "
"
       ]
@@ -97,7 +97,7 @@
     },
     {
      "data": {
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LFixQTU2NvvjiC+3evdvrOEgSvI3CTec4js6ePau0tDT9+eefXsdBkmBlg5uuoaFBjz/+eGKT5KVNZpK0f/9+vf/++5o4caIyMzNVWFjoXVCYYlMfABOsbDCs6urqdPHiRUnSjBkzVFZW5nEieIWVDQATXCAGYIKyAWCCsgFg4t8Z2MzVTag6UwAAAABJRU5ErkJggg==",
       "text/plain": [
        "
"
       ]
@@ -230,19 +230,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iteration: 1 \t BC Loss: 0.4289\t PDE Loss: 0.1653 \t Loss: 0.5942\n",
-      "Iteration: 501 \t BC Loss: 0.0210\t PDE Loss: 0.0000 \t Loss: 0.0210\n",
-      "Iteration: 1001 \t BC Loss: 0.0000\t PDE Loss: 0.0000 \t Loss: 0.0000\n",
-      "Iteration: 1501 \t BC Loss: 0.0000\t PDE Loss: 0.0000 \t Loss: 0.0000\n",
-      "Iteration: 2001 \t BC Loss: 0.0000\t PDE Loss: 0.0000 \t Loss: 0.0000\n",
-      "Iteration: 2501 \t BC Loss: 0.0000\t PDE Loss: 0.0000 \t Loss: 0.0000\n",
-      "Iteration: 3001 \t BC Loss: 0.0000\t PDE Loss: 0.0000 \t Loss: 0.0000\n",
-      "Iteration: 3501 \t BC Loss: 0.0000\t PDE Loss: 0.0000 \t Loss: 0.0000\n",
-      "Iteration: 4001 \t BC Loss: 0.0000\t PDE Loss: 0.0000 \t Loss: 0.0000\n",
-      "Iteration: 4501 \t BC Loss: 0.0000\t PDE Loss: 0.0000 \t Loss: 0.0000\n",
-      "Iteration: 5001 \t BC Loss: 0.0000\t PDE Loss: 0.0000 \t Loss: 0.0000\n",
+      "Iteration: 1 \t BC Loss: 0.6485\t PDE Loss: 56.3284 \t Loss: 56.9768\n",
+      "Iteration: 501 \t BC Loss: 0.4976\t PDE Loss: 0.9739 \t Loss: 1.4716\n",
+      "Iteration: 1001 \t BC Loss: 0.3631\t PDE Loss: 0.0074 \t Loss: 0.3705\n",
+      "Iteration: 1501 \t BC Loss: 0.3169\t PDE Loss: 0.0015 \t Loss: 0.3184\n",
+      "Iteration: 2001 \t BC Loss: 0.2724\t PDE Loss: 0.0013 \t Loss: 0.2738\n",
+      "Iteration: 2501 \t BC Loss: 0.2183\t PDE Loss: 0.0008 \t Loss: 0.2191\n",
+      "Iteration: 3001 \t BC Loss: 0.1593\t PDE Loss: 0.0002 \t Loss: 0.1595\n",
+      "Iteration: 3501 \t BC Loss: 0.1047\t PDE Loss: 0.0000 \t Loss: 0.1048\n",
+      "Iteration: 4001 \t BC Loss: 0.0621\t PDE Loss: 0.0002 \t Loss: 0.0623\n",
+      "Iteration: 4501 \t BC Loss: 0.0330\t PDE Loss: 0.0003 \t Loss: 0.0333\n",
+      "Iteration: 5001 \t BC Loss: 0.0152\t PDE Loss: 0.0002 \t Loss: 0.0154\n",
       "Training finished\n",
-      "Time taken: 'trainer' in 29.4198 secs\n"
+      "Time taken: 'trainer' in 29.1206 secs\n"
      ]
     }
    ],
@@ -321,7 +321,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -332,15 +332,15 @@
    ],
    "source": [
     "plt.figure(1)\n",
-    "plt.scatter(coordinates_list[0][:,0], coordinates_list[0][:,1], c=solution_list[0][:,0], label = \"collocation points\",  cmap=plt.get_cmap('plasma', 10))\n",
-    "plt.scatter(coordinates_list[1][:,0], coordinates_list[1][:,1], c=solution_list[1], label = \"boundary points\", cmap=plt.get_cmap('plasma', 10))\n",
+    "plt.scatter(coordinates_list[0][:,0], coordinates_list[0][:,1], c=solution_list[0][:,0], label = \"collocation points\",  cmap=plt.get_cmap('plasma', 10), edgecolors='k')\n",
+    "plt.scatter(coordinates_list[1][:,0], coordinates_list[1][:,1], c=solution_list[1], label = \"boundary points\", cmap=plt.get_cmap('plasma', 10),edgecolors='k')\n",
     "plt.colorbar()\n",
     "plt.grid('minor')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -349,13 +349,13 @@
        "Text(0, 0.5, 'Loss')"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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/P1+DBw/WunXrjnkev98vn88X8oo1T8fwkq+JHhkAAKKpW0Fm69atuuCCCyRJzzzzjEaPHq1//vOfevrpp7Vs2bKTOteWLVuUkZEht9ut2bNna+XKlRo1apSqq6vlcrmUlZUVcnx2draqq6uPeb758+fL6/Var7y8vJP9emHzpLVP+GVoCQCA6OpWkGlpaZHb7ZYkvfbaa/rmN78pqb3HpKqq6qTOdeaZZ2rz5s3asGGDbrnlFl1//fXavn17d8qSJM2bN091dXXWq7Kystvn6i5Px51LPhbFAwAgqpK786GzzjpLTzzxhK688kqtWbNGv/jFLyRJu3fvVt++fU/qXC6XS8OHD5ckjR8/Xhs3btQjjzyiGTNmqLm5WbW1tSG9MjU1NcrJyTnm+dxutxWy7EKPDAAAsdGtHpnf/OY3+t3vfqdJkyZp5syZGjdunCTphRdesIacuisQCMjv92v8+PFKSUlRSUmJta+8vFwVFRUqKCgI6xrRxhwZAABio1s9MpMmTdK+ffvk8/nUu3dva/vNN9+s9PT0Ez7PvHnzNGXKFA0ePFj19fVavny51q5dq1dffVVer1c33XST5s6dqz59+sjj8ej2229XQUHBCd+xZJfDPTIMLQEAEE3dCjKHDh2SMcYKMZ999plWrlypkSNHqqio6ITPs2fPHl133XWqqqqS1+vV2LFj9eqrr+ryyy+XJD300ENyOp2aPn26/H6/ioqKtGjRou6UHFPBOTJ1DC0BABBV3QoyU6dO1bRp0zR79mzV1tZqwoQJSklJ0b59+/Tggw/qlltuOaHzLFmy5Lj7U1NTtXDhQi1cuLA7ZdrGk8bQEgAAsdCtOTLvvvuuLrnkEknSc889p+zsbH322Wd68skn9eijj0a0wERk3bVEjwwAAFHVrSBz8OBBZWZmSpL+/ve/a9q0aXI6nZo4caI+++yziBaYiKw5Mtx+DQBAVHUryAwfPlyrVq1SZWWlXn31VV1xxRWS2ue8xOoxAPHMumuJHhkAAKKqW0Hm7rvv1l133aWhQ4fqggsusG6H/vvf/65zzjknogUmosM9MgQZAACiqVuTfb/1rW/p4osvVlVVlbWGjCRNnjxZ11xzTcSKS1TBOTIN/lYFAkZOp8PmigAA6Jm6FWQkKScnRzk5Ofr8888lSYMGDQp7MbyeIrNjaMkYqd7fKm9HDw0AAIisbg0tBQIB/fznP5fX69WQIUM0ZMgQZWVl6Re/+IUCgUCka0w4qSlJcie3Ny3zZAAAiJ5u9cj85Cc/0ZIlS7RgwQJddNFFkqS33npL9957r5qamvSrX/0qokUmIk9aivbW+5knAwBAFHUryPzpT3/SH/7wB+up15I0duxYnXbaabr11lsJMmq/c2lvvZ/HFAAAEEXdGlo6cOCA8vPzj9ien5+vAwcOhF1UTxC8c4nHFAAAED3dCjLjxo3T448/fsT2xx9/XGPHjg27qJ7AWt2XoSUAAKKmW0NL999/v6688kq99tpr1hoy69atU2VlpV5++eWIFpioDj8BmyADAEC0dKtH5qtf/ao+/PBDXXPNNaqtrVVtba2mTZumbdu26amnnop0jQmJ1X0BAIi+bq8jk5ube8Sk3vfff19LlizR//7v/4ZdWKLLSmeODAAA0datHhl8ud7pLknSFwcJMgAARAtBJkqyrCDTbHMlAAD0XASZKMnqmOxbS48MAABRc1JzZKZNm3bc/bW1teHU0qP07tURZA7RIwMAQLScVJDxer1fuv+6664Lq6CeIji0VNtIjwwAANFyUkFm6dKl0aqjxwlO9q33t6qlLaCUJEbxAACINP66RklwHRmJeTIAAEQLQSZKkpOcVpipY54MAABRQZCJot69WEsGAIBoIshEkbWWTCM9MgAARANBJop6p7OWDAAA0USQiSJrUTzmyAAAEBUEmSjK4nlLAABEFUEmioJrydTyvCUAAKKCIBNFwccUfMHqvgAARAVBJoq8zJEBACCqCDJRdHhoiR4ZAACigSATRb2tyb70yAAAEA0EmSjK6lhH5ouDLTLG2FwNAAA9D0EmioKPKGhuDehQS5vN1QAA0PMQZKKolytJruT2Jt7fwPASAACRRpCJIofDoX4dvTL7ed4SAAARR5CJsr4ZbknS/ga/zZUAANDzEGSirG9GR48MQ0sAAEQcQSbK+vbq6JFhaAkAgIgjyERZP6tHhqElAAAijSATZdbQEj0yAABEHEEmyoJDS/vokQEAIOIIMlHGZF8AAKKHIBNl/YK3XzfSIwMAQKQRZKKsc48Mz1sCACCyCDJR1qdjZd/WgJHvUKvN1QAA0LMQZKLMnZykTHeyJGkfw0sAAEQUQSYGmPALAEB02Bpk5s+fr/PPP1+ZmZkaMGCArr76apWXl4cc09TUpOLiYvXt21cZGRmaPn26ampqbKq4e3jeEgAA0WFrkCktLVVxcbHWr1+vNWvWqKWlRVdccYUaGxutY+688069+OKLevbZZ1VaWqrdu3dr2rRpNlZ98vp2zJPZx6J4AABEVLKdF1+9enXI+2XLlmnAgAEqKyvTpZdeqrq6Oi1ZskTLly/XZZddJklaunSpRo4cqfXr12vixIl2lH3Sgj0yBxhaAgAgouJqjkxdXZ0kqU+fPpKksrIytbS0qLCw0DomPz9fgwcP1rp16456Dr/fL5/PF/KyW/B5S6zuCwBAZMVNkAkEApozZ44uuugijR49WpJUXV0tl8ulrKyskGOzs7NVXV191PPMnz9fXq/XeuXl5UW79C81ILO9R2ZPfZPNlQAA0LPETZApLi7W1q1btWLFirDOM2/ePNXV1VmvysrKCFXYfQM8qZKkGh89MgAARJKtc2SCbrvtNr300kt68803NWjQIGt7Tk6OmpubVVtbG9IrU1NTo5ycnKOey+12y+12R7vkk2L1yPjokQEAIJJs7ZExxui2227TypUr9frrr2vYsGEh+8ePH6+UlBSVlJRY28rLy1VRUaGCgoJYl9tt2R09Mnsb/AoEeEwBAACRYmuPTHFxsZYvX67nn39emZmZ1rwXr9ertLQ0eb1e3XTTTZo7d6769Okjj8ej22+/XQUFBQlzx5Ik9e/okWlpM/riYLN1FxMAAAiPrUFm8eLFkqRJkyaFbF+6dKluuOEGSdJDDz0kp9Op6dOny+/3q6ioSIsWLYpxpeFJSXKqby+X9jc2a0+9nyADAECE2BpkTuRp0KmpqVq4cKEWLlwYg4qiZ4AnVfsbm1Xja9LIgR67ywEAoEeIm7uWerrDE365cwkAgEghyMRItoe1ZAAAiDSCTIxks5YMAAARR5CJEVb3BQAg8ggyMcLqvgAARB5BJkZY3RcAgMgjyMQIq/sCABB5BJkY6bq6LwAACB9BJkZSkpxWmKmqY3gJAIBIIMjEUK63fXhpd+0hmysBAKBnIMjEUG5WmiSCDAAAkUKQiSEryDC0BABARBBkYigYZP5NjwwAABFBkImh07KYIwMAQCQRZGKIOTIAAEQWQSaGgkFmT71fza0Bm6sBACDxEWRiqG8vl1zJThkj1fCoAgAAwkaQiSGHw8FaMgAARBBBJsYO34JNkAEAIFwEmRg7POGXoSUAAMJFkImxYJD5/At6ZAAACBdBJsYGWUHmoM2VAACQ+AgyMTa4b7okqeIAQQYAgHARZGJsSEeQ+fcXh9TaxloyAACEgyATY9mZqXIlO9UaMKri4ZEAAISFIBNjTqdDeb3b58l8tp/hJQAAwkGQscGQvr0kSZ8daLS5EgAAEhtBxgaD+3RM+KVHBgCAsBBkbBCc8MvQEgAA4SHI2MDqkeEWbAAAwkKQscGQTmvJGGNsrgYAgMRFkLHBoN7pcjikBn+rDjQ2210OAAAJiyBjg9SUJA30pEqSdu3nziUAALqLIGOT0/tnSJI+2UOQAQCguwgyNhk+oCPI7G2wuRIAABIXQcYmX+nfvijex3sIMgAAdBdBxiZf6U+PDAAA4SLI2CQ4tFRx4KD8rW02VwMAQGIiyNikf6Zbme5kBYy0ax8L4wEA0B0EGZs4HA59hQm/AACEhSBjI2ueDBN+AQDoFoKMjb4yoOPOJXpkAADoFoKMjUYMyJQklVfX21wJAACJiSBjo/yB7UHm4z0Nam4N2FwNAACJhyBjo9Oy0uRJTVZrwLAwHgAA3UCQsZHD4VD+QI8kaUeVz+ZqAABIPAQZm40iyAAA0G0EGZuN7Jgns6OaIAMAwMmyNci8+eabuuqqq5SbmyuHw6FVq1aF7DfG6O6779bAgQOVlpamwsJCffTRR/YUGyUjrR6ZehljbK4GAIDEYmuQaWxs1Lhx47Rw4cKj7r///vv16KOP6oknntCGDRvUq1cvFRUVqampKcaVRs+I7Ew5HdKBxmbtrffbXQ4AAAkl2c6LT5kyRVOmTDnqPmOMHn74Yf30pz/V1KlTJUlPPvmksrOztWrVKn3nO9+JZalRk5qSpNP7Z+jjPQ3aurtOl3lS7S4JAICEEbdzZHbu3Knq6moVFhZa27xeryZMmKB169Yd83N+v18+ny/kFe/GDvJKkjZX1tlcCQAAiSVug0x1dbUkKTs7O2R7dna2te9o5s+fL6/Xa73y8vKiWmcknJOXJUl6v7LW1joAAEg0cRtkumvevHmqq6uzXpWVlXaX9KXGBYPM57VM+AUA4CTEbZDJycmRJNXU1IRsr6mpsfYdjdvtlsfjCXnFu/wcj1zJTtUebNFn+w/aXQ4AAAkjboPMsGHDlJOTo5KSEmubz+fThg0bVFBQYGNlkedKduqs3PbA9f7ntfYWAwBAArE1yDQ0NGjz5s3avHmzpPYJvps3b1ZFRYUcDofmzJmjX/7yl3rhhRe0ZcsWXXfddcrNzdXVV19tZ9lRMW5QliTpvYpaW+sAACCR2Hr79aZNm/S1r33Nej937lxJ0vXXX69ly5bphz/8oRobG3XzzTertrZWF198sVavXq3U1J53i/LZHfNkNjPhFwCAE+YwPXx2qc/nk9frVV1dXVzPl6nYf1CXPvCGUpIc+uCeIqW5kuwuCQAA25zo3++4nSNzqsnrk6aB3lS1tBm9W/GF3eUAAJAQCDJxwuFwaMKwPpKk9Z/ut7kaAAASA0Emjkw4va8kacOnB2yuBACAxECQiSMTO4LM5spaNbW02VwNAADxjyATR4b2TdeATLea2wLMkwEA4AQQZOKIw+GwemXWfcI8GQAAvgxBJs5cfEY/SVLph3ttrgQAgPhHkIkzk0b0lyR98Hmd9jX4ba4GAID4RpCJMwM8qdZzl96kVwYAgOMiyMShSWe298q8UU6QAQDgeAgycehrZw6QJP3jo71qC/ToJ0gAABAWgkwcOjsvS1npKao92KKNu1gcDwCAYyHIxKHkJKcuH5ktSXp5S5XN1QAAEL8IMnHq62MHSpJe2VrN8BIAAMdAkIlTF32lnzypydpb79cmhpcAADgqgkycciU7dfmoHEkMLwEAcCwEmTj2jXHtw0svflCl5taAzdUAABB/CDJx7JLh/TQg060Djc0q2VFjdzkAAMQdgkwcS05yavr4QZKkZzZV2lwNAADxhyAT5759Xp6k9odIVtc12VwNAADxhSAT54b166ULhvZRwEh/2UivDAAAnRFkEsCsiYMlSU+t36WmljabqwEAIH4QZBLA18cMVK43VfsamvXC+7vtLgcAgLhBkEkAKUlO3XDRUEnSkn/slDGs9AsAgESQSRgzzh+sXq4kldfU67Ude+wuBwCAuECQSRDetBRdd+FQSdKDaz5UgOcvAQBAkEkk/33p6cp0J2tHlU+vbK22uxwAAGxHkEkgWeku3XTJMEnSb9eU89gCAMApjyCTYG66eJj6Zbj06d5GLX17p93lAABgK4JMgslMTdGP/iNfkvRoyUes9gsAOKURZBLQ9HMH6dzBWWpsbtN9L27jdmwAwCmLIJOAnE6HfnH1aCU7HXpla7VWbf633SUBAGALgkyCOivXqzsmnyFJuvv5bfp37SGbKwIAIPYIMgnslklf0dl5WapvatWtT7/Lc5gAAKccgkwCS05y6tHvnKOs9BS9X1mrn67aynwZAMAphSCT4Ab3TdfjM8+V0yE9V/a5Fq39xO6SAACIGYJMD3DxGf30s2+MkiQ98Gq5nlq3y96CAACIEYJMD3HjRcN0+2XDJUk/e36bnlr/mc0VAQAQfQSZHmTu5SN040VDJUk/W7VVD675kDkzAIAejSDTgzgcDt39jVHWbdmPlnyk2//8nhr8rTZXBgBAdBBkehiHw6E7Lx+hX13TvmDeSx9U6ZuPv6Vtu+vsLg0AgIgjyPRQsyYM0V/+e6JyPKn6dG+jvvn427p/9b9YawYA0KMQZHqw8UP66G//72J9fUyO2gJGi9Z+osIHS/XXdz9XW4C5MwCAxOcwPXw2qM/nk9frVV1dnTwej93l2Gb11mrd88JW1fj8kqQR2Rn63iWn66pxuUpNSbK5OgAAQp3o32+CzCnkUHOblv5zpxav/UT1Te0TgPtluPSf5+Vp6tm5ys85tdsHABA/CDIdCDJHqjvYouXvVOjJdbtUVddkbR+RnaGis3J08fB+Omdwb7mSGXkEANiDINOBIHNsLW0Brdleo1Xv/Vtry/equS1g7evlStK5Q3pr3KAsjR3k1ZhBXuV4UuVwOGysGABwqiDIdCDInJi6Qy1as71Gb364V299vE8HGpuPOKaXK0nD+vfSsH4ZGtY3XTneNGV73Mr2pGpAplt9M9xKchJ0AADh61FBZuHChXrggQdUXV2tcePG6bHHHtMFF1xwQp8lyJy8QMBoe5VPmytr9cHntfrg8zp9WFOvL7vRyeGQMtzJ8qSmyJOWoszUjt9Tk+VOSVJqilPu5KP/TElyKsnpUJLToWSnQ86On+3vnUpySklOp7Ut+HI6HHJI7T8d7TUEf7e2ySFn5/ed9js79nf+bPCcwWMBALF3on+/k2NYU7f85S9/0dy5c/XEE09owoQJevjhh1VUVKTy8nINGDDA7vJ6JKfTodGneTX6NK+kIZIkf2ubKg8c1Kd7G7VzX6N27T+oPb4m1dQ3aY/Pr30NfgWMVN/UqvqmVv279pC9XyKCuoYgZ6dw5OgSmoLhSF3Ck7MjEDmdOkr4OnysOrY7nUe/RufgdURgs/YHtwX3dz0meP2Oa3Y5t9N59PpDQqEzGPaCNXa6pjqd7wTrd3YkR+uax2gf63wh1wg9T9fvdDiYHr32rt+xa12h1zsy9LbXHvreIYcczqN/Z6ejc7sRlIFwxX2PzIQJE3T++efr8ccflyQFAgHl5eXp9ttv149//OMv/Tw9MrHR2hbQgYPNqm9qle9QS/vPphb5DrWqvqlFTS0B+VvbrJ/+1oCaWg7/bG0zagsYtQYCajNSWyBgbWsLGLUZ0+kY074/YCQjBYyRUfvPgNER2+L7XzhOdV2DTeegfPQwFxpQu4bkzj2KoeHrOOfUiQW6I2p0Hj0cHxmij1Hzl4TLI2p0dqrtGDV3DrdWb2tHezjU8Uayrhfcp07HHH7v6HRs6Plkfa7rts49uh3X7DjJUa/Z+fNH2Rb8Lp3rOO41O7Ye+zsd2S7WVbvUaO3pVJdCPnN4f1a6SxnuyPaN9IgemebmZpWVlWnevHnWNqfTqcLCQq1bt+6on/H7/fL7/dZ7n88X9TohJSc5NSAzVQMy7a7k6ExHyLF+qj3gBINOMPiYQPu+QKd9pktQMtb2w59r60hQ7du6nNcc55yB49QT/FygU31WDcF62lPa4c+1/y7TqV6F1t31e3QNfV2vceT3Dx7T5Zo6XFNw+xHXVOg1Qr5X4Di1qus1j/Udjqyt6+e6fudA4Nj/jQOd6lSX953/e4QTmNv/6Ri1r7lN6kZi+vU1Y/RfEwbbcu24DjL79u1TW1ubsrOzQ7ZnZ2frX//611E/M3/+fN13332xKA8JxOFwKKnz/8sDREHXYHO04GQCXYJUl2B8tPft89MOnzsY+rqGuuP+DAnTXUP3kQH5iCDd5RzBABYwxw6AVo2B0BqOHzaPFaBPIlyGhP/2tus43PrvFNzXeZusbYeDaef3HadSxxmtbdZnO5+z4xpHnONErtmpRh1lW/B467Pm8PmOes1O23S8urtcs/OvnY89fK7DdSTZuFpHXAeZ7pg3b57mzp1rvff5fMrLy7OxIgCnimBgTiIwAzET10GmX79+SkpKUk1NTcj2mpoa5eTkHPUzbrdbbrc7FuUBAACbxfXSrS6XS+PHj1dJSYm1LRAIqKSkRAUFBTZWBgAA4kFc98hI0ty5c3X99dfrvPPO0wUXXKCHH35YjY2NuvHGG+0uDQAA2Czug8yMGTO0d+9e3X333aqurtbZZ5+t1atXHzEBGAAAnHrifh2ZcLGODAAAiedE/37H9RwZAACA4yHIAACAhEWQAQAACYsgAwAAEhZBBgAAJCyCDAAASFgEGQAAkLAIMgAAIGERZAAAQMKK+0cUhCu4cLHP57O5EgAAcKKCf7e/7AEEPT7I1NfXS5Ly8vJsrgQAAJys+vp6eb3eY+7v8c9aCgQC2r17tzIzM+VwOCJ2Xp/Pp7y8PFVWVvIMpyijrWODdo4N2jk2aOfYiGY7G2NUX1+v3NxcOZ3HngnT43tknE6nBg0aFLXzezwe/o8kRmjr2KCdY4N2jg3aOTai1c7H64kJYrIvAABIWAQZAACQsAgy3eR2u3XPPffI7XbbXUqPR1vHBu0cG7RzbNDOsREP7dzjJ/sCAICeix4ZAACQsAgyAAAgYRFkAABAwiLIAACAhEWQ6aaFCxdq6NChSk1N1YQJE/TOO+/YXVJce/PNN3XVVVcpNzdXDodDq1atCtlvjNHdd9+tgQMHKi0tTYWFhfroo49Cjjlw4IBmzZolj8ejrKws3XTTTWpoaAg55oMPPtAll1yi1NRU5eXl6f7774/2V4sb8+fP1/nnn6/MzEwNGDBAV199tcrLy0OOaWpqUnFxsfr27auMjAxNnz5dNTU1IcdUVFToyiuvVHp6ugYMGKAf/OAHam1tDTlm7dq1Ovfcc+V2uzV8+HAtW7Ys2l8vrixevFhjx461FgErKCjQK6+8Yu2nnSNvwYIFcjgcmjNnjrWNdo6Me++9Vw6HI+SVn59v7Y/7djY4aStWrDAul8v88Y9/NNu2bTPf+973TFZWlqmpqbG7tLj18ssvm5/85Cfmr3/9q5FkVq5cGbJ/wYIFxuv1mlWrVpn333/ffPOb3zTDhg0zhw4dso75j//4DzNu3Dizfv16849//MMMHz7czJw509pfV1dnsrOzzaxZs8zWrVvNn//8Z5OWlmZ+97vfxepr2qqoqMgsXbrUbN261WzevNl8/etfN4MHDzYNDQ3WMbNnzzZ5eXmmpKTEbNq0yUycONFceOGF1v7W1lYzevRoU1hYaN577z3z8ssvm379+pl58+ZZx3z66acmPT3dzJ0712zfvt089thjJikpyaxevTqm39dOL7zwgvnb3/5mPvzwQ1NeXm7+53/+x6SkpJitW7caY2jnSHvnnXfM0KFDzdixY80dd9xhbaedI+Oee+4xZ511lqmqqrJee/futfbHezsTZLrhggsuMMXFxdb7trY2k5uba+bPn29jVYmja5AJBAImJyfHPPDAA9a22tpa43a7zZ///GdjjDHbt283kszGjRutY1555RXjcDjMv//9b2OMMYsWLTK9e/c2fr/fOuZHP/qROfPMM6P8jeLTnj17jCRTWlpqjGlv05SUFPPss89ax+zYscNIMuvWrTPGtAdOp9NpqqurrWMWL15sPB6P1a4//OEPzVlnnRVyrRkzZpiioqJof6W41rt3b/OHP/yBdo6w+vp6c8YZZ5g1a9aYr371q1aQoZ0j55577jHjxo076r5EaGeGlk5Sc3OzysrKVFhYaG1zOp0qLCzUunXrbKwsce3cuVPV1dUhber1ejVhwgSrTdetW6esrCydd9551jGFhYVyOp3asGGDdcyll14ql8tlHVNUVKTy8nJ98cUXMfo28aO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