From f41959ccb2d9d4c722fe8fc3351401d53bcf4900 Mon Sep 17 00:00:00 2001
From: Manjunath Kudlur <keveman@gmail.com>
Date: Fri, 6 Nov 2015 16:27:58 -0800
Subject: TensorFlow: Initial commit of TensorFlow library. TensorFlow is an
 open source software library for numerical computation using data flow
 graphs.

Base CL: 107276108
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 tensorflow/g3doc/tutorials/pdes/index.md | 129 +++++++++++++++++++++++++++++++
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+
+## Basic Setup
+
+
+```
+#Import libraries for simulation
+import tensorflow as tf
+import numpy as np
+
+#Imports for visualization
+import PIL.Image
+from cStringIO import StringIO
+from IPython.display import clear_output, Image, display
+```
+
+
+```
+def DisplayArray(a, fmt='jpeg', rng=[0,1]):
+  """Display an array as a picture."""
+  a = (a - rng[0])/float(rng[1] - rng[0])*255
+  a = np.uint8(np.clip(a, 0, 255))
+  f = StringIO()
+  PIL.Image.fromarray(a).save(f, fmt)
+  display(Image(data=f.getvalue()))
+```
+
+
+```
+sess = tf.InteractiveSession()
+```
+
+## Computational Convenience Functions
+
+
+```
+def make_kernel(a):
+  """Transform a 2D array into a convolution kernel"""
+  a = np.asarray(a)
+  a = a.reshape(list(a.shape) + [1,1])
+  return tf.constant(a, dtype=1)
+
+def simple_conv(x, k):
+  """A simplified 2D convolution operation"""
+  x = tf.expand_dims(tf.expand_dims(x, 0), -1)
+  y = tf.nn.depthwise_conv2d(x, k, [1, 1, 1, 1], padding='SAME')
+  return y[0, :, :, 0]
+
+def laplace(x):
+  """Compute the 2D laplacian of an array"""
+  laplace_k = make_kernel([[0.5, 1.0, 0.5],
+                           [1.0, -6., 1.0],
+                           [0.5, 1.0, 0.5]])
+  return simple_conv(x, laplace_k)
+```
+
+## Define the PDE
+
+
+```
+N = 500
+```
+
+
+```
+# Initial Conditions -- some rain drops hit a pond
+
+# Set everything to zero
+u_init = np.zeros([N, N], dtype="float32")
+ut_init = np.zeros([N, N], dtype="float32")
+
+# Some rain drops hit a pond at random points
+for n in range(40):
+  a,b = np.random.randint(0, N, 2)
+  u_init[a,b] = np.random.uniform()
+  
+DisplayArray(u_init, rng=[-0.1, 0.1])
+```
+
+
+![jpeg](output_8_0.jpe)
+
+
+
+```
+# paramaters
+# eps -- time resolution
+# damping -- wave damping
+eps = tf.placeholder('float', shape=())
+damping = tf.placeholder('float', shape=())
+
+# create variables for simulation state
+U  = tf.Variable(u_init)
+Ut = tf.Variable(ut_init)
+
+# discretized PDE update rules
+U_ = U + eps*Ut
+Ut_ = Ut + eps*(laplace(U) - damping*Ut)
+
+# operation to update the state
+step = tf.group(
+  U.Assign(U_),
+  Ut.Assign(Ut_) )
+```
+
+## Run The Simulation
+
+
+```
+# initialize state to initial conditions
+tf.InitializeAllVariables().Run()
+
+# Run 1000 steps of PDE
+for i in range(1000):
+  # Step simulation
+  step.Run({eps: 0.03, damping: 0.04})
+  # Visualize every 50 steps
+  if i % 50 == 0:
+    clear_output()
+    DisplayArray(U.eval(), rng=[-0.1, 0.1])
+```
+
+
+![jpeg](output_11_0.jpe)
+
+
+
+```
+
+```
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