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# Partial Differential Equations

TensorFlow isn't just for machine learning. Here we give a (somewhat pedestrian) example of using TensorFlow for simulating the behavior of a partial differential equation. We'll simulate the surface of square pond as a few raindrops land on it.

## Basic Setup

A few imports we'll need.

``````#Import libraries for simulation
import tensorflow as tf
import numpy as np

#Imports for visualization
import PIL.Image
from io import BytesIO
from IPython.display import clear_output, Image, display
``````

A function for displaying the state of the pond's surface as an image.

``````def DisplayArray(a, fmt='jpeg', rng=[0,1]):
"""Display an array as a picture."""
a = (a - rng)/float(rng - rng)*255
a = np.uint8(np.clip(a, 0, 255))
f = BytesIO()
PIL.Image.fromarray(a).save(f, fmt)
clear_output(wait = True)
display(Image(data=f.getvalue()))
``````

Here we start an interactive TensorFlow session for convenience in playing around. A regular session would work as well if we were doing this in an executable .py file.

``````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

Our pond is a perfect 500 x 500 square, as is the case for most ponds found in nature.

``````N = 500
``````

Here we create our pond and hit it with some rain drops.

``````# Initial Conditions -- some rain drops hit a pond

# Set everything to zero
u_init = np.zeros([N, N], dtype=np.float32)
ut_init = np.zeros([N, N], dtype=np.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])
`````` Now let's specify the details of the differential equation.

``````# Parameters:
# eps -- time resolution
# damping -- wave damping
eps = tf.placeholder(tf.float32, shape=())
damping = tf.placeholder(tf.float32, 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

This is where it gets fun -- running time forward with a simple for loop.

``````# Initialize state to initial conditions
tf.global_variables_initializer().run()

# Run 1000 steps of PDE
for i in range(1000):
# Step simulation
step.run({eps: 0.03, damping: 0.04})
DisplayArray(U.eval(), rng=[-0.1, 0.1])
`````` Look! Ripples!