<a name="step-25-Intro"></a> <h1>Introduction</h1>

This program grew out of a student project by Ivan Christov at Texas A&amp;M
University. Most of the work for this program is by him.

The goal of this program is to solve the sine-Gordon soliton equation
in 1, 2 or 3 spatial dimensions. The motivation for solving this
equation is that very little is known about the nature of the
solutions in 2D and 3D, even though the 1D case has been studied
extensively.

Rather facetiously, the sine-Gordon equation's moniker is a pun on the
so-called Klein-Gordon equation, which is a relativistic version of
the Schrödinger equation for particles with non-zero mass. The resemblance is not just
superficial, the sine-Gordon equation has been shown to model some
unified-field phenomena such as interaction of subatomic particles
(see, e.g., Perring &amp; Skyrme in Nuclear %Physics <b>31</b>) and the
Josephson (quantum) effect in superconductor junctions (see, e.g., <a
href="http://en.wikipedia.org/wiki/Long_Josephson_junction">http://en.wikipedia.org/wiki/Long_Josephson_junction</a>).
Furthermore, from the mathematical standpoint, since the sine-Gordon
equation is "completely integrable," it is a candidate for study using
the usual methods such as the inverse scattering
transform. Consequently, over the years, many interesting
solitary-wave, and even stationary, solutions to the sine-Gordon
equation have been found. In these solutions, particles correspond to
localized features. For more on the sine-Gordon equation, the
inverse scattering transform and other methods for finding analytical
soliton equations, the reader should consult the following "classical"
references on the subject: G. L. Lamb's <i>Elements of Soliton
Theory</i> (Chapter 5, Section 2) and G. B. Whitham's <i>Linear and
Nonlinear Waves</i> (Chapter 17, Sections 10-13).

@note We will cover a separate nonlinear equation from quantum
  mechanics, the Nonlinear Schr&ouml;dinger Equation, in step-58.

<h3>Statement of the problem</h3>
The sine-Gordon initial-boundary-value problem (IBVP) we wish to solve
consists of the following equations:
\f{eqnarray*}
  u_{tt}-\Delta u &=& -\sin(u) \quad\mbox{for}\quad (x,t) \in \Omega \times (t_0,t_f],\\
  {\mathbf n} \cdot \nabla u &=& 0 \quad\mbox{for}\quad (x,t) \in \partial\Omega
           \times (t_0,t_f],\\
  u(x,t_0) &=& u_0(x).
\f}
It is a nonlinear equation similar to the wave equation we
discussed in step-23 and step-24.
We have chosen to enforce zero Neumann boundary conditions in order for waves
to reflect off the boundaries of our domain. It should be noted, however, that
Dirichlet boundary conditions are not appropriate for this problem. Even
though the solutions to the sine-Gordon equation are localized, it only makes
sense to specify (Dirichlet) boundary conditions at $x=\pm\infty$, otherwise
either a solution does not exist or only the trivial solution $u=0$ exists.

However, the form of the equation above is not ideal for numerical
discretization. If we were to discretize the second-order time
derivative directly and accurately, then  we would need a large
stencil (i.e., several time steps would need to be kept in the
memory), which could become expensive. Therefore, in complete analogy
to what we did in step-23 and step-24,
we split the
second-order (in time) sine-Gordon equation into a system of two
first-order (in time) equations, which we call the split, or velocity,
formulation. To this end, by setting $v = u_t$, it is easy to see that the sine-Gordon equation is equivalent to
\f{eqnarray*}
  u_t - v &=& 0,\\
  v_t - \Delta u &=& -\sin(u).
\f}

<h3>Discretization of the equations in time</h3>
Now, we can discretize the split formulation in time using the
$\theta$-method, which has a stencil of only two time steps. By
choosing a $\theta\in [0,1]$, the latter discretization allows us to
choose from a continuum of schemes. In particular, if we pick
$\theta=0$ or $\theta=1$, we obtain the first-order accurate explicit
or implicit Euler method, respectively. Another important choice is
$\theta=\frac{1}{2}$, which gives the second-order accurate
Crank-Nicolson scheme. Henceforth, a superscript $n$ denotes the
values of the variables at the $n^{\mathrm{th}}$ time step, i.e. at
$t=t_n \dealcoloneq n k$, where $k$ is the (fixed) time step size. Thus,
the split formulation of the time-discretized sine-Gordon equation becomes
\f{eqnarray*}
  \frac{u^n - u^{n-1}}{k} - \left[\theta v^n + (1-\theta) v^{n-1}\right] &=& 0,\\
  \frac{v^n - v^{n-1}}{k} - \Delta\left[\theta u^n + (1-\theta) u^{n-1}\right]
  &=& -\sin\left[\theta u^n + (1-\theta) u^{n-1}\right].
\f}

We can simplify the latter via a bit of algebra. Eliminating $v^n$ from the first equation and rearranging, we obtain
\f{eqnarray*}
  \left[ 1-k^2\theta^2\Delta \right] u^n &=&
         \left[ 1+k^2\theta(1-\theta)\Delta\right] u^{n-1} + k v^{n-1}
         - k^2\theta\sin\left[\theta u^n + (1-\theta) u^{n-1}\right],\\
   v^n &=& v^{n-1} + k\Delta\left[ \theta u^n + (1-\theta) u^{n-1}\right]
         - k\sin\left[ \theta u^n + (1-\theta) u^{n-1} \right].
\f}

It may seem as though we can just proceed to discretize the equations
in space at this point. While this is true for the second equation
(which is linear in $v^n$), this would not work for all $\theta$ since the
first equation above is nonlinear. Therefore, a nonlinear solver must be
implemented, then the equations can be discretized in space and solved.

To this end, we can use Newton's method. Given the nonlinear equation $F(u^n) = 0$, we produce successive approximations to $u^n$ as follows:
\f{eqnarray*}
  \mbox{ Find } \delta u^n_l \mbox{ s.t. } F'(u^n_l)\delta u^n_l = -F(u^n_l)
  \mbox{, set }  u^n_{l+1} = u^n_l + \delta u^n_l.
\f}
The iteration can be initialized with the old time step, i.e. $u^n_0 = u^{n-1}$,
and eventually it will produce a solution to the first equation of
the split formulation (see above). For the time discretization of the
sine-Gordon equation under consideration here, we have that
\f{eqnarray*}
  F(u^n_l) &=&  \left[ 1-k^2\theta^2\Delta \right] u^n_l -
                 \left[ 1+k^2\theta(1-\theta)\Delta\right] u^{n-1} - k v^{n-1}
                 + k^2\theta\sin\left[\theta u^n_l + (1-\theta) u^{n-1}\right],\\
  F'(u^n_l) &=& 1-k^2\theta^2\Delta + k^2\theta^2\cos\left[\theta u^n_l
                        + (1-\theta) u^{n-1}\right].
\f}
Notice that while $F(u^n_l)$ is a function, $F'(u^n_l)$ is an operator.

<h3>Weak formulation of the time-discretized equations</h3>
With hindsight, we choose both the solution and the test space to be $H^1(\Omega)$. Hence, multiplying by a test function $\varphi$ and integrating, we obtain the following variational (or weak) formulation of the split formulation (including the nonlinear solver for the first equation) at each time step:
\f{eqnarray*}
  &\mbox{ Find}& \delta u^n_l \in H^1(\Omega) \mbox{ s.t. }
  \left( F'(u^n_l)\delta u^n_l, \varphi \right)_{\Omega}
  = -\left(F(u^n_l), \varphi \right)_{\Omega} \;\forall\varphi\in H^1(\Omega),
  \mbox{ set } u^n_{l+1} = u^n_l + \delta u^n_l,\; u^n_0 = u^{n-1}.\\
  &\mbox{ Find}& v^n \in H^1(\Omega) \mbox{ s.t. }
  \left( v^n, \varphi \right)_{\Omega} = \left( v^{n-1}, \varphi \right)_{\Omega}
         - k\theta\left( \nabla u^n, \nabla\varphi \right)_{\Omega}
         - k (1-\theta)\left( \nabla u^{n-1}, \nabla\varphi \right)_{\Omega}
         - k\left(\sin\left[ \theta u^n + (1-\theta) u^{n-1} \right],
         \varphi \right)_{\Omega} \;\forall\varphi\in H^1(\Omega).
\f}
Note that the we have used integration by parts and the zero Neumann
boundary conditions on all terms involving the Laplacian
operator. Moreover, $F(\cdot)$ and $F'(\cdot)$ are as defined above,
and $(\cdot,\cdot)_{\Omega}$ denotes the usual $L^2$ inner product
over the domain $\Omega$, i.e. $(f,g)_{\Omega} = \int_\Omega fg
\,\mathrm{d}x$. Finally, notice that the first equation is, in fact,
the definition of an iterative procedure, so it is solved multiple
times during each time step until a stopping criterion is met.

<h3>Discretization of the weak formulation in space</h3>
Using the Finite Element Method, we discretize the variational
formulation in space. To this end, let $V_h$ be a finite-dimensional
$H^1(\Omega)$-conforming finite element space ($\mathrm{dim}\, V_h = N
< \infty$) with nodal basis $\{\varphi_1,\ldots,\varphi_N\}$. Now,
we can expand all functions in the weak formulation (see above) in
terms of the nodal basis. Henceforth, we shall denote by a capital
letter the vector of coefficients (in the nodal basis) of a function
denoted by the same letter in lower case; e.g., $u^n = \sum_{i=1}^N
U^n_i \varphi_i$ where $U^n \in {R}^N$ and $u^n \in
H^1(\Omega)$. Thus, the finite-dimensional version of the variational formulation requires that we solve the following matrix equations at each time step:
@f{eqnarray*}{
  F_h'(U^{n,l})\delta U^{n,l} &=& -F_h(U^{n,l}), \qquad
        U^{n,l+1} = U^{n,l} + \delta U^{n,l}, \qquad U^{n,0} = U^{n-1}; \\
  MV^n &=& MV^{n-1} - k \theta AU^n -k (1-\theta) AU^{n-1} - k S(u^n,u^{n-1}).
@f}
Above, the matrix $F_h'(\cdot)$ and the vector $F_h(\cdot)$ denote the discrete versions of the gadgets discussed above, i.e.,
\f{eqnarray*}
  F_h(U^{n,l}) &=&  \left[ M+k^2\theta^2A \right] U^{n,l} -
                \left[ M-k^2\theta(1-\theta)A \right] U^{n-1} - k MV^{n-1}
                + k^2\theta S(u^n_l, u^{n-1}),\\
  F_h'(U^{n,l}) &=& M+k^2\theta^2A
                                + k^2\theta^2N(u^n_l,u^{n-1})
\f}
Again, note that the first matrix equation above is, in fact, the
definition of an iterative procedure, so it is solved multiple times
until a stopping criterion is met. Moreover, $M$ is the @ref GlossMassMatrix "mass matrix",
i.e. $M_{ij} = \left( \varphi_i,\varphi_j \right)_{\Omega}$, $A$ is
the Laplace matrix, i.e. $A_{ij} = \left( \nabla \varphi_i, \nabla
\varphi_j \right)_{\Omega}$, $S$ is the nonlinear term in the
equation that defines our auxiliary velocity variable, i.e. $S_j(f,g) = \left(
  \sin\left[ \theta f + (1-\theta) g\right], \varphi_j \right)_{\Omega}$, and
$N$ is the nonlinear term in the Jacobian matrix of $F(\cdot)$,
i.e. $N_{ij}(f,g) = \left( \cos\left[ \theta f + (1-\theta) g\right]\varphi_i,
  \varphi_j \right)_{\Omega}$.

What solvers can we use for the first equation? Let's look at the matrix we
have to invert:
@f[
  (M+k^2\theta^2(A + N))_{ij} =
  \int_\Omega (1+k^2\theta^2 \cos \alpha)
  \varphi_i\varphi_j \; dx
  + k^2 \theta^2 \int_\Omega \nabla\varphi_i\nabla\varphi_j \; dx,
@f]
for some $\alpha$ that depends on the present and previous solution. First,
note that the matrix is symmetric. In addition, if the time step $k$ is small
enough, i.e. if $k\theta<1$, then the matrix is also going to be positive
definite. In the program below, this will always be the case, so we will use
the Conjugate Gradient method together with the SSOR method as
preconditioner. We should keep in mind, however, that this will fail
if we happen to use a bigger time step. Fortunately, in that case
the solver will just throw an exception indicating a failure to converge,
rather than silently producing a wrong result. If that happens, then we can
simply replace the CG method by something that can handle indefinite symmetric
systems. The GMRES solver is typically the standard method for all "bad"
linear systems, but it is also a slow one. Possibly better would be a solver
that utilizes the symmetry, such as, for example, SymmLQ, which is also
implemented in deal.II.

This program uses a clever optimization over step-23 and @ref
step_24 "step-24": If you read the above formulas closely, it becomes clear
that the velocity $V$ only ever appears in products with the mass matrix. In
step-23 and step-24, we were, therefore, a bit
wasteful: in each time step, we would solve a linear system with the mass
matrix, only to multiply the solution of that system by $M$ again in the next
time step. This can, of course, be avoided, and we do so in this program.


<h3>The test case</h3>

There are a few analytical solutions for the sine-Gordon equation, both in 1D
and 2D. In particular, the program as is computes the solution to a problem
with a single kink-like solitary wave initial condition.  This solution is
given by Leibbrandt in \e Phys. \e Rev. \e Lett. \b 41(7), and is implemented
in the <code>ExactSolution</code> class.

It should be noted that this closed-form solution, strictly speaking, only holds
for the infinite-space initial-value problem (not the Neumann
initial-boundary-value problem under consideration here). However, given that
we impose \e zero Neumann boundary conditions, we expect that the solution to
our initial-boundary-value problem would be close to the solution of the
infinite-space initial-value problem, if reflections of waves off the
boundaries of our domain do \e not occur. In practice, this is of course not
the case, but we can at least assume that this were so.

The constants $\vartheta$ and $\lambda$ in the 2D solution and $\vartheta$,
$\phi$ and $\tau$ in the 3D solution are called the B&auml;cklund
transformation parameters. They control such things as the orientation and
steepness of the kink. For the purposes of testing the code against the exact
solution, one should choose the parameters so that the kink is aligned with
the grid.

The solutions that we implement in the <code>ExactSolution</code> class are
these:
<ul>
  <li>In 1D:
  @f[
  u(x,t) =
  -4 \arctan\left[
     \frac{m}{\sqrt{1-m^2}}
     \frac{\sin\left(\sqrt{1-m^2}t+c_2\right)}
     {\cosh\left(mx+c_1\right)}
     \right],
  @f]
  where we choose $m=\frac 12, c_1=c_2=0$.

  In 1D, more interesting analytical solutions are known. Many of them are
  listed on http://mathworld.wolfram.com/Sine-GordonEquation.html .

  <li>In 2D:
  @f[
    u(x,y,t) = 4 \arctan \left[a_0 e^{s\xi}\right],
  @f]
  where $\xi$ is defined as
  @f[
    \xi = x \cos\vartheta + \sin(\vartheta) (y\cosh\lambda + t\sinh \lambda),
  @f]
  and where we choose $\vartheta=\frac \pi 4, \lambda=a_0=s=1$.

  <li>In 3D:
  @f[
    u(x,y,z,t) = 4 \arctan \left[c_0 e^{s\xi}\right],
  @f]
  where $\xi$ is defined as
  @f[
    \xi = x \cos\vartheta + y \sin \vartheta \cos\phi +
          \sin \vartheta \sin\phi (z\cosh\tau + t\sinh \tau),
  @f]
  and where we choose $\vartheta=\phi=\frac{\pi}{4}, \tau=c_1=s=1$.
</ul>


Since it makes it easier to play around, the <code>InitialValues</code> class
that is used to set &mdash; surprise! &mdash; the initial values of our
simulation simply queries the class that describes the exact solution for the
value at the initial time, rather than duplicating the effort to implement a
solution function.
