# Introduction

## A Laplace problem

As an introduction we will solve \begin{equation} -\triangle u + u = f \end{equation} in \(\Omega=[0,1]^2\), where \(f=f(x)\) is a given forcing term. On the boundary we prescribe Neumann boundary \(\nabla u\cdot n = 0\).

We will solve this problem in variational form \(a(u,v) = l(v)\) with \begin{equation} a(u,v) := \int_\Omega \nabla u\cdot\nabla v + uv~,\qquad l(v) := \int_\Omega fv~. \end{equation} We choose \(f=(8\pi^2+1)\cos(2\pi x_1)\cos(2\pi x_2)\) so that the exact solution is \begin{align*} u(x) = \cos(2\pi x_1)\cos(2\pi x_2) \end{align*}

We first need to setup a tessellation of \(\Omega\). We use a Cartesian grid with a 20 cells in each coordinate direction

```
[1]:
```

```
import matplotlib
matplotlib.rc( 'image', cmap='jet' )
import numpy as np
from dune.grid import structuredGrid
gridView = structuredGrid([0, 0], [1, 1], [20, 20])
```

Next we define a linear Lagrange Finite-Element space over that grid and setup a discrete function which we will store the discrete solution to our PDE

```
[2]:
```

```
from dune.fem.space import lagrange
space = lagrange(gridView, order=1)
u_h = space.interpolate(0, name='u_h')
```

We define the mathematical problem using ufl

```
[3]:
```

```
from ufl import (TestFunction, TrialFunction, SpatialCoordinate,
dx, grad, inner, dot, sin, cos, pi )
x = SpatialCoordinate(space)
u = TrialFunction(space)
v = TestFunction(space)
f = (8*pi**2+1) * cos(2*pi*x[0])*cos(2*pi*x[1])
a = ( inner(grad(u),grad(v)) + u*v ) * dx
l = f*v * dx
```

Now we can assemble the matrix and the right hand side

```
[4]:
```

```
from dune.fem import assemble
mat,rhs = assemble(a==l)
```

We solve the resulting linear system of equations \(Ay=b\) using scipy. To this end it is straightforward to expose the underlying data structures in \(A,b,u_h\) using the `as_numpy`

attribute. More details on how to use Scipy and also PetSC4py will be discussed later in the tutorial.

```
[5]:
```

```
from scipy.sparse.linalg import spsolve as solver
A = mat.as_numpy
b = rhs.as_numpy
y = u_h.as_numpy
y[:] = solver(A,b)
```

Note the `y[:]`

which guarantees that the result from the solver is stored in the same buffer used for the discrete function. Consequently, no copying is required.

So that’s it - to see the result we plot it using matplotlib

```
[6]:
```

```
u_h.plot()
```

Since in this case the exact solution is known, we can also compute the \(L^2\) and \(H^1\) errors to see how good our approximation actually is

```
[7]:
```

```
from dune.fem.function import integrate
exact = cos(2*pi*x[0])*cos(2*pi*x[1])
e_h = u_h-exact
squaredErrors = integrate(gridView, [e_h**2,inner(grad(e_h),grad(e_h))], order=5)
print("L^2 and H^1 errors:",[np.sqrt(e) for e in squaredErrors])
```

```
L^2 and H^1 errors: [0.00481699658937062, 0.4026000450756809]
```

## Laplace equation with Dirichlet boundary conditions

We consider the scalar boundary value problem \begin{align*} -\triangle u &= f & \text{in}\;\Omega:=(0,1)^2 \\ \nabla u\cdot n &= g_N & \text{on}\;\Gamma_N \\ u &= g_D & \text{on}\;\Gamma_D \end{align*} and \(f=f(x)\) is some forcing term. For the boundary conditions we set \(\Gamma_D={0}\times[0,1]\) and take \(\Gamma_N\) to be the remaining boundary of \(\Omega\).

We will solve this problem in variational form \begin{align*} \int \nabla u \cdot \nabla \varphi \ - \int_{\Omega} f(x) \varphi\ dx - \int_{\Gamma_N} g_N(x) v\ ds = 0. \end{align*} We choose \(f,g_N,g_D\) so that the exact solution is \begin{align*} u(x) = \left(\frac{1}{2}(x_1^2 + x_2^2) - \frac{1}{3}(x_1^3 - x_2^3)\right) + 1~. \end{align*} Note: in a later section we discuss more general boundary conditions.

The setup of the model using ufl is very similar to the previous example but we need to include the non trivial Neumann boundary conditions:

```
[8]:
```

```
from ufl import conditional, FacetNormal, ds, div
exact = 1/2*(x[0]**2+x[1]**2) - 1/3*(x[0]**3 - x[1]**3) + 1
a = dot(grad(u), grad(v)) * dx
f = -div( grad(exact) )
g_N = grad(exact)
n = FacetNormal(space)
l = f*v*dx + dot(g_N,n)*conditional(x[0]>=1e-8,1,0)*v*ds
```

With the model described as a ufl form, we can again assemble the system matrix and right hand side using `dune.fem.assemble`

. To take the Dirichlet boundary conditions into account we construct an instance of `dune.ufl.DirichletBC`

that described the values to use and the part of the boundary to apply them to. This is then passed into the `assemble`

function:

```
[9]:
```

```
from dune.ufl import DirichletBC
dbc = DirichletBC(space,exact,x[0]<=1e-8)
mat,rhs = assemble([a==l,dbc])
```

Solving the linear system of equations, plotting the solution, and computing the error is now identical to the previous example:

```
[10]:
```

```
u_h.as_numpy[:] = solver(mat.as_numpy, rhs.as_numpy)
u_h.plot()
e_h = u_h-exact
squaredErrors = integrate(gridView, [e_h**2,inner(grad(e_h),grad(e_h))], order=5)
print("L^2 and H^1 errors:",[np.sqrt(e) for e in squaredErrors])
```

```
L^2 and H^1 errors: [0.0004929215389252552, 0.031176023550870714]
```

## A Non-linear elliptic problem

It is very easy to solve a non-linear elliptic problem with very few changes to the above code. We will demonstrate this using the PDE \begin{equation} -\triangle u + m(u) = f \end{equation} in \(\Omega=[0,1]^2\), where again \(f=f(x)\) is a given forcing term and \(m=m(u)\) is some non-linearity. On the boundary we still prescribe Neumann boundary \(\nabla u\cdot n = 0\).

We will solve this problem in variational form \begin{equation}
\int_\Omega \nabla u\cdot\nabla v + m(u)v = \int_\Omega fv~.
\end{equation} We keep the same forcing \(f(x)=|x|^2\) as before and choose \(m(u) = (1+u)^2u\). Most of the code is identical to the linear case, we can use the same grid, discrete lagrange space, and the discrete function `u_h`

. The model description using ufl is also very similar

```
[11]:
```

```
a = ( inner(grad(u),grad(v)) + (1+u)**2*u*v ) * dx
l = dot(x,x)*v * dx
```

To solve the non-linear problem we need to use something like a Newton solver. We could use the implementation available in Scipy but `dune-fem`

provides so called `schemes`

that have a `solve`

method which can handle both linear and non-linear models. The default method is a Newton-Krylov solver using a `gmres`

method to solve the intermediate linear problems. Since the problem here is symmetric we can use a `cg`

method. A full list of available solvers,
preconditioners, and how to customize them is available here

```
[12]:
```

```
from dune.fem.scheme import galerkin as solutionScheme
scheme = solutionScheme(a == l, solver='cg')
u_h.clear() # set u_h to zero as initial guess for the Newton solver
info = scheme.solve(target=u_h)
```

That’s it - we can plot the solution again - we don’t know the exact solution so we can’t compute any errors in this case. In addition the `info`

structured returned by the `solve`

method gives some information on the solver step

```
[13]:
```

```
print(info)
u_h.plot()
```

```
{'converged': True, 'iterations': 5, 'linear_iterations': 250}
```

A wide range of problems a covered in in the further examples section. In the next section we explain the main concepts we use to solve PDE using finite-element approximations which we end with a solution to a non-linear time-dependent problem using the Crank-Nicholson method in time.