Exercises (1)

Exercise 1: Linear algebra refresher I

Look up the topic of vector space in your favorite linear algebra book or search for the term at Wikipedia. Prove that vectors in the plane \((a,b)\) form a vector space by showing that all the axioms of a vector space are satisfied. Similarly, prove that all linear functions of the form \(ax+b\) constitute a vector space, \(a,b\in\mathbb{R}\).

On the contrary, show that all quadratic functions of the form \(1 + ax^2 + bx\) do not constitute a vector space. Filename: linalg1.pdf.

Exercise 2: Linear algebra refresher II

As an extension of Exercise 1: Linear algebra refresher I, check out the topic of inner product spaces. Suggest a possible inner product for the space of all linear functions of the form \(ax+b\), \(a,b\in\mathbb{R}\). Show that this inner product satisfies the general requirements of an inner product in a vector space. Filename: linalg2.pdf.

Exercise 3: Approximate a three-dimensional vector in a plane

Given \(\boldsymbol{f} = (1,1,1)\) in \(\mathbb{R}^3\), find the best approximation vector \(\boldsymbol{u}\) in the plane spanned by the unit vectors \((1,0)\) and \((0,1)\). Repeat the calculations using the vectors \((2,1)\) and \((1,2)\). Filename: vec111_approx.pdf.

Exercise 4: Approximate the exponential function by power functions

Let \(V\) be a function space with basis functions \(x^i\), \(i=0,1,\ldots,N\). Find the best approximation to \(f(x)=\exp(-x)\) on \(\Omega =[0,4]\) among all functions in \(V\) for \(N=2,4,6\). Illustrate the three approximations in three separate plots. Add the corresponding Taylor polynomial approximation of degree \(N\) in each plot. Filename: exp_powers.py.

Exercise 5: Approximate the sine function by power functions

Let \(V\) be a function space with basis functions \(x^{2i+1}\), \(i=0,1,\ldots,N\). Find the best approximation to \(f(x)=\sin(x)\) among all functions in \(V\), using \(N=8\) for a domain that includes more and more half-periods of the sine function: \(\Omega = [0, k\pi/2]\), \(k=2,3,\ldots,12\). How does a Taylor series of \(\sin(x)\) around \(x\) up to degree 9 behave for the largest domain?

Hint. One can make a loop over \(k\) and call the functions least_squares and comparison_plot from the approx1D module.

Filename: sin_powers.py.

Exercise 6: Approximate a steep function by sines

Find the best approximation of \(f(x) = \tanh (s(x-\pi))\) on \([0, 2\pi]\) in the space \(V\) with basis \({\psi}_i(x) = \sin((2i+1)x)\), \(i\in{\mathcal{I}_s} = \{0,\ldots,N\}\). Make a movie showing how \(u=\sum_{j\in{\mathcal{I}_s}}c_j{\psi}_j(x)\) approximates \(f(x)\) as \(N\) grows. Choose \(s\) such that \(f\) is steep (\(s=20\) may be appropriate).

Hint. One may naively call the least_squares_orth and comparison_plot from the approx1D module in a loop and extend the basis with one new element in each pass. This approach implies a lot of recomputations. A more efficient strategy is to let least_squares_orth compute with only one basis function at a time and accumulate the corresponding u in the total solution.

Filename: tanh_sines_approx1.py.

Exercise 7: Animate the approximation of a steep function by sines

Make a movie where the steepness (\(s\)) of the \(\tanh\) function in Exercise 6: Approximate a steep function by sines grows in “time”, and for each value of the steepness, the movie shows how the approximation improves with increasing \(N\). Filename: tanh_sines_approx2.py.

Exercise 8: Fourier series as a least squares approximation

Given a function \(f(x)\) on an interval \([0,L]\), look up the formula for the coefficients \(a_j\) and \(b_j\) in the Fourier series of \(f\):

\[f(x) = a_0 + \sum_{j=1}^\infty a_j\cos \left(j\frac{\pi x}{L}\right) + \sum_{j=1}^\infty b_j\sin \left(j\frac{\pi x}{L}\right){\thinspace .}\]

Let an infinite-dimensional vector space \(V\) have the basis functions \(\cos j\frac{\pi x}{L}\) for \(j=0,1,\dots,\infty\) and \(\sin j\frac{\pi x}{L}\) for \(j=1,\dots,\infty\). Show that the least squares approximation method from the section Approximation of functions leads to a linear system whose solution coincides with the standard formulas for the coefficients in a Fourier series of \(f(x)\) (see also the section Fourier series). You may choose

\[{\psi}_{2i} = \cos\left( i\frac{\pi}{L}x\right),\quad {\psi}_{2i+1} = \sin\left( i\frac{\pi}{L}x\right),\]

for \(i=0,1,\ldots,N\rightarrow\infty\).

Choose \(f(x) = \tanh(s(x-\frac{1}{2}))\) on \(\Omega=[0,1]\), which is a smooth function, but with considerable steepness around \(x=1/2\) as \(s\) grows in size. Calculate the coefficients in the Fourier expansion by solving the linear system, arising from the least squares or Galerkin methods, by hand. Plot some truncated versions of the series together with \(f(x)\) to show how the series expansion converges for \(s=10\) and \(s=100\). Filename: Fourier_approx.py.

Exercise 9: Approximate a steep function by Lagrange polynomials

Use interpolation/collocation with uniformly distributed points and Chebychev nodes to approximate

\[f(x) = -\tanh(s(x-\frac{1}{2})),\quad x\in [0,1],\]

by Lagrange polynomials for \(s=10\) and \(s=100\), and \(N=3,6,9,11\). Make separate plots of the approximation for each combination of \(s\), point type (Chebyshev or uniform), and \(N\). Filename: tanh_Lagrange.py.

Exercise 10: Define nodes and elements

Consider a domain \(\Omega =[0,2]\) divided into the three P2 elements \([0,1]\), \([1,1.2]\), and \([1.2,2]\).

For P1 and P2 elements, set up the list of coordinates and nodes (nodes) and the numbers of the nodes that belong to each element (elements) in two cases: 1) nodes and elements numbered from left to right, and 2) nodes and elements numbered from right to left. Filename: fe_numberings1.py..

Exercise 11: Define vertices, cells, and dof maps

Repeat Exercise 10: Define nodes and elements, but define the data structures vertices, cells, and dof_map instead of nodes and elements. Filename: fe_numberings2.py.

Exercise 12: Construct matrix sparsity patterns

Exercise 10: Define nodes and elements describes a element mesh with a total of five elements, but with two different element and node orderings. For each of the two orderings, make a \(5\times 5\) matrix and fill in the entries that will be nonzero.

Hint. A matrix entry \((i,j)\) is nonzero if \(i\) and \(j\) are nodes in the same element.

Filename: fe_sparsity_pattern.pdf.

Exercise 13: Perform symbolic finite element computations

Perform hand calculations to find formulas for the coefficient matrix and right-hand side when approximating \(f(x) = \sin (x)\) on \(\Omega=[0, \pi]\) by two P1 elements of size \(\pi/2\). Solve the system and compare \(u(\pi/2)\) with the exact value 1.

Filename: sin_approx_P1.py.

Exercise 14: Approximate a steep function by P1 and P2 elements

Given

\[f(x) = \tanh(s(x-\frac{1}{2}))\]

use the Galerkin or least squares method with finite elements to find an approximate function \(u(x)\). Choose \(s=40\) and try \(N_e=4,8,16\) P1 elements and \(N_e=2,4,8\) P2 elements. Integrate \(f{\varphi}_i\) numerically. Filename: tanh_fe_P1P2_approx.py.

Exercise 15: Approximate a steep function by P3 and P4 elements

Solve fem:approx:exer:tanh using \(N_e=1,2,4\) P3 and P4 elements. How will a collocation/interpolation method work in this case with the same number of nodes? Filename: tanh_fe_P3P4_approx.py.

Exercise 16: Investigate the approximation error in finite elements

The theory (7.1) from the section fem:approx:fe:error predicts that the error in the Pd approximation of a function should behave as \(h^{d+1}\). Use experiments to verify this asymptotic behavior (i.e., for small enough \(h\)). Choose two examples: \(f(x)=Ae^{-\omega x}\) on \([0,3/\omega]\) and \(f(x) = A\sin (\omega x)\) on \(\Omega=[0, 2\pi/\omega]\) for constants \(A\) and \(\omega\). What happens if you try \(f(x)=\sqrt{x}\) on \([0,1]\)?

Hint. Run a series of experiments: \((h_i,E_)\), \(i=0,\ldots,m\), where \(E_i\) is the \(L^2\) norm of the error corresponding to element length \(h_i\). Assume an error model \(E=Ch^r\) and compute \(r\) from two successive experiments:

\[r_i = \ln (E_{i+1}/E_i)/\ln (h_{i+1}/h_i),\quad i=0,\ldots,m-1{\thinspace .}\]

Hopefully, the sequence \(r_0,\ldots,r_{m-1}\) converges to the true \(r\), and \(r_{m-1}\) can be taken as an approximation to \(r\).

Filename: Asinwt_interpolation_error.py.

Exercise 17: Approximate a step function by finite elements

Approximate the step function

\[\begin{split}f(x) = \left\lbrace\begin{array}{ll} 1 & x < {1/2},\\ 2 & x \geq {1/2} \end{array}\right.\end{split}\]

by 2, 4, and 8 P1 and P2 elements. Compare approximations visually.

Hint. This \(f\) can also be expressed in terms of the Heaviside function \(H(x)\): \(f(x) = H(x-{1/2})\). Therefore, \(f\) can be defined by

f = sp.Heaviside(x -  sp.Rational(1,2))

making the approximate function in the fe_approx1D.py module an obvious candidate to solve the problem. However, sympy does not handle symbolic integration with this particular integrand, and the approximate function faces a problem when converting f to a Python function (for plotting) since Heaviside is not an available function in numpy. It is better to make special-purpose code for this case or perform all calculations by hand.

Filename: Heaviside_approx_P1P2.py..

Exercise 18: 2D approximation with orthogonal functions

Assume we have basis functions \({\varphi}_i(x,y)\) in 2D that are orthogonal such that \(({\varphi}_i,{\varphi}_j)=0\) when \(i\neq j\). The function least_squares in the file approx2D.py will then spend much time on computing off-diagonal terms in the coefficient matrix that we know are zero. To speed up the computations, make a version least_squares_orth that utilizes the orthogonality among the basis functions. Apply the function to approximate

\[f(x,y) = x(1-x)y(1-y)e^{-x-y}\]

on \(\Omega = [0,1]\times [0,1]\) via basis functions

\[{\varphi}_i(x,y) = \sin (p\pi x)\sin(q\pi y),\quad i=q N_x + p {\thinspace .}\]

Hint. Get ideas from the function least_squares_orth in the section Orthogonal basis functions and file approx1D.py.

Filename: approx2D_lsorth_sin.py.

Exercise 19: Use the Trapezoidal rule and P1 elements

Consider approximation of some \(f(x)\) on an interval \(\Omega\) using the least squares or Galerkin methods with P1 elements. Derive the element matrix and vector using the Trapezoidal rule (8.1) for calculating integrals on the reference element. Assemble the contributions, assuming a uniform cell partitioning, and show that the resulting linear system has the form \(c_i=f(x_{i})\) for \(i\in{\mathcal{I}_s}\). Filename: fe_P1_trapez.pdf.

Problem 20: Compare P1 elements and interpolation

We shall approximate the function

\[f(x) = 1 + \epsilon\sin (2\pi nx),\quad x\in \Omega = [0,1],\]

where \(n\in\mathbb{Z}\) and \(\epsilon \geq 0\).

a) Sketch \(f(x)\) and find the wave length of the function.

b) We want to use \(N_P\) elements per wave length. Show that the number of elements is then \(nN_P\).

c) The critical quantity for accuracy is the number of elements per wave length, not the element size in itself. It therefore suffices to study an \(f\) with just one wave length in \(\Omega = [0,1]\). Set \(\epsilon = 0.5\).

Run the least squares or projection/Galerkin method for \(N_P=2,4,8,16,32\). Compute the error \(E=||u-f||_{L^2}\).

Hint. Use the fe_approx1D_numint module to compute \(u\) and use the technique from the section Computing the error of the approximation to compute the norm of the error.

d) Repeat the set of experiments in the above point, but use interpolation/collocation based on the node points to compute \(u(x)\) (recall that \(c_i\) is now simply \(f(x_{i})\)). Compute the error \(E=||u-f||_{L^2}\). Which method seems to be most accurate?

Filename: P1_vs_interp.py.

Exercise 21: Implement 3D computations with global basis functions

Extend the approx2D.py code to 3D applying ideas from the section Extension to 3D. Use a 3D generalization of the test problem in the section Implementation (3) to test the implementation. Filename: approx3D.py.

Exercise 22: Use Simpson’s rule and P2 elements

Redo Exercise 19: Use the Trapezoidal rule and P1 elements, but use P2 elements and Simpson’s rule based on sampling the integrands at the nodes in the reference cell.

Filename: fe_P2_simpson.pdf.