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Numerical methods challenge: Day 24

During October (2017) I will write a program per day for some well-known numerical methods in both Python and Julia. It is intended to be an exercise then don't expect the code to be good enough for real use. Also, I should mention that I have almost no experience with Julia, so it probably won't be idiomatic Julia but more Python-like Julia.

Finite element method

Today we have the Finite element method to solve the equation:

\begin{equation*} \frac{d^2 u}{dx^2} = f(x) \end{equation*}


\begin{equation*} u(0) = u(1) = 0 \end{equation*}

As in the Ritz method we form a functional that is equivalent to the differential equation, propose an approximation as a linear combination of a set of basis functions and find the best set of coefficients for that combination. That best solution is found minimizing the functional.

The functional for this differential equation is

\begin{equation*} \Pi[u] = -\int_{0}^{1} \left(\frac{d u}{d x}\right)^2 dx -\int_{0}^{1} u f(x) dx \end{equation*}

The main difference is that we use a piecewise interpolation for the basis functions,

\begin{equation*} \hat{u}(x) = \sum_{n=0}^{N} u_n N_n(x)\, , \end{equation*}

this leads to the system of equations

\begin{equation*} [K]\{\mathbf{c}\} = \{\mathbf{b}\} \end{equation*}

where the local stiffness matrices read

\begin{equation*} K_\text{local} = \frac{1}{|J|}\begin{bmatrix} 2 & -2\\ -2 &2\end{bmatrix} \end{equation*}


\begin{equation*} b_\text{local} = -|J|\begin{bmatrix} f(x_m)\\ f(x_{n})\end{bmatrix}\, , \end{equation*}

where \(|J|\) is the Jacobian determinant of the transformation. I am skipping a great deal about assembling, but it would be just too extensive to describe the complete process.

We will test the implementation with the function \(f(x) = x^3\), that leads to the solution

\begin{equation*} u(x) = \frac{x (x^4 - 1)}{20} \end{equation*}

Following are the codes.


from __future__ import division, print_function
import numpy as np
import matplotlib.pyplot as plt

def FEM1D(coords, source):
    N = len(coords)
    stiff_loc = np.array([[2.0, -2.0], [-2.0, 2.0]])
    eles = [np.array([cont, cont + 1]) for cont in range(0, N - 1)]
    stiff = np.zeros((N, N))
    rhs = np.zeros(N)
    for ele in eles:
        jaco = coords[ele[1]] - coords[ele[0]]
        rhs[ele] = rhs[ele] + jaco*source(coords[ele])
        for cont1, row in enumerate(ele):
            for cont2, col in enumerate(ele):
                stiff[row, col] = stiff[row, col] +  stiff_loc[cont1, cont2]/jaco
    return stiff, rhs

N = 100
fun = lambda x: x**3
x = np.linspace(0, 1, N)
stiff, rhs = FEM1D(x, fun)
sol = np.zeros(N)
sol[1:-1] = np.linalg.solve(stiff[1:-1, 1:-1], -rhs[1:-1])

#%% Plotting
plt.figure(figsize=(4, 3))
plt.plot(x, sol)
plt.plot(x, x*(x**4 - 1)/20, linestyle="dashed")
plt.legend(["FEM solution", "Exact solution"])


using PyPlot

function FEM1D(coords, source)
    N = length(coords)
    stiff_loc = [2.0 -2.0; -2.0 2.0]
    eles = [[cont, cont + 1] for cont in 1:N-1]
    stiff = zeros(N, N)
    rhs = zeros(N)
    for ele in eles
        jaco = coords[ele[2]] - coords[ele[1]]
        rhs[ele] = rhs[ele] + jaco*source(coords[ele])
        stiff[ele, ele] = stiff[ele, ele] +  stiff_loc/jaco
    return stiff, rhs

N = 100
fun(x) = x.^3
x = linspace(0, 1, N)
stiff, rhs = FEM1D(x, fun)
sol = zeros(N)
sol[2:end-1] = -stiff[2:end-1, 2:end-1]\rhs[2:end-1]

#%% Plotting
figure(figsize=(4, 3))
plot(x, sol)
plot(x, x.*(x.^4 - 1)/20, linestyle="dashed")
legend(["FEM solution", "Exact solution"])

Both have the same result, as follows

Finite element method approximation.

Comparison Python/Julia

Regarding number of lines we have: 37 in Python and 35 in Julia. The comparison in execution time is done with %timeit magic command in IPython and @benchmark in Julia. For the test we are just comparing the time it takes to generate the matrices.

For Python:

%timeit FEM1D(x, fun)

with result

100 loops, best of 3: 2.15 ms per loop

For Julia:

@benchmark FEM1D(x, fun)

with result

  memory estimate:  183.73 KiB
  allocs estimate:  1392
  minimum time:     60.045 μs (0.00% GC)
  median time:      70.445 μs (0.00% GC)
  mean time:        98.276 μs (25.64% GC)
  maximum time:     4.269 ms (96.70% GC)
  samples:          10000
  evals/sample:     1

In this case, we can say that the Python code is roughly 30 times slower than Julia code.


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