Poisson equation¶
This demo is implemented in a single Python file,
demo_poisson.py
, which contains both the variational forms
and the solver.
This demo illustrates how to:
Solve a linear partial differential equation
Create and apply Dirichlet boundary conditions
Define a FunctionSpace
The solution for \(u\) in this demo will look as follows:

Equation and problem definition¶
The Poisson equation is the canonical elliptic partial differential equation. For a domain \(\Omega \subset \mathbb{R}^n\) with boundary \(\partial \Omega = \Gamma_{D} \cup \Gamma_{N}\), the Poisson equation with particular boundary conditions reads:
Here, \(f\) and \(g\) are input data and \(n\) denotes the outward directed boundary normal. The most standard variational form of Poisson equation reads: find \(u \in V\) such that
where \(V\) is a suitable function space and
The expression \(a(u, v)\) is the bilinear form and \(L(v)\) is the linear form. It is assumed that all functions in \(V\) satisfy the Dirichlet boundary conditions (\(u = 0 \ {\rm on} \ \Gamma_{D}\)).
In this demo, we shall consider the following definitions of the input functions, the domain, and the boundaries:
\(\Omega = [0,1] \times [0,1]\) (a unit square)
\(\Gamma_{D} = \{(0, y) \cup (1, y) \subset \partial \Omega\}\) (Dirichlet boundary)
\(\Gamma_{N} = \{(x, 0) \cup (x, 1) \subset \partial \Omega\}\) (Neumann boundary)
\(g = \sin(5x)\) (normal derivative)
\(f = 10\exp(-((x - 0.5)^2 + (y - 0.5)^2) / 0.02)\) (source term)
Implementation¶
This description goes through the implementation (in
demo_poisson.py
) of a solver for the above described
Poisson equation step-by-step.
First, the dolfinx
module is imported:
import matplotlib.pyplot as plt
import numpy as np
from mpi4py import MPI
from petsc4py import PETSc
import dolfinx
import dolfinx.plotting
import ufl
from dolfinx import DirichletBC, Function, FunctionSpace, RectangleMesh, solve
from dolfinx.cpp.mesh import CellType
from dolfinx.fem import locate_dofs_topological
from dolfinx.io import XDMFFile
from dolfinx.mesh import locate_entities_boundary
from ufl import ds, dx, grad, inner
We begin by defining a mesh of the domain and a finite element
function space \(V\) relative to this mesh. As the unit square is
a very standard domain, we can use a built-in mesh provided by the
class UnitSquareMesh
. In order
to create a mesh consisting of 32 x 32 squares with each square
divided into two triangles, we do as follows
# Create mesh and define function space
mesh = RectangleMesh(
MPI.COMM_WORLD,
[np.array([0, 0, 0]), np.array([1, 1, 0])], [32, 32],
CellType.triangle, dolfinx.cpp.mesh.GhostMode.none)
V = FunctionSpace(mesh, ("Lagrange", 1))
The second argument to FunctionSpace
is the finite element
family, while the third argument specifies the polynomial
degree. Thus, in this case, our space V
consists of first-order,
continuous Lagrange finite element functions (or in order words,
continuous piecewise linear polynomials).
Next, we want to consider the Dirichlet boundary condition. A simple
Python function, returning a boolean, can be used to define the
boundary for the Dirichlet boundary condition (\(\Gamma_D\)). The
function should return True
for those points inside the boundary
and False
for the points outside. In our case, we want to say that
the points \((x, y)\) such that \(x = 0\) or \(x = 1\) are
inside on the inside of \(\Gamma_D\). (Note that because of
rounding-off errors, it is often wise to instead specify \(x <
\epsilon\) or \(x > 1 - \epsilon\) where \(\epsilon\) is a
small number (such as machine precision).)
Now, the Dirichlet boundary condition can be created using the class
DirichletBC
. A
DirichletBC
takes two
arguments: the value of the boundary condition
and the part of the boundary on which the condition applies.
This boundary part is identified with degrees of
freedom in the function space to which we apply the boundary conditions.
A method locate_dofs_geometrical
is provided to extract the boundary
degrees of freedom using a geometrical criterium.
In our example, the function space is V
,
the value of the boundary condition (0.0) can represented using a
Function
and the Dirichlet
boundary is defined immediately above. The definition of the Dirichlet
boundary condition then looks as follows:
# Define boundary condition on x = 0 or x = 1
u0 = Function(V)
u0.vector.set(0.0)
facets = locate_entities_boundary(mesh, 1,
lambda x: np.logical_or(x[0] < np.finfo(float).eps,
x[0] > 1.0 - np.finfo(float).eps))
bc = DirichletBC(u0, locate_dofs_topological(V, 1, facets))
Next, we want to express the variational problem. First, we need to
specify the trial function \(u\) and the test function \(v\),
both living in the function space \(V\). We do this by defining a
TrialFunction
and a TestFunction
on the previously defined
FunctionSpace
V
.
Further, the source \(f\) and the boundary normal derivative \(g\) are involved in the variational forms, and hence we must specify these.
With these ingredients, we can write down the bilinear form a
and
the linear form L
(using UFL operators). In summary, this reads
# Define variational problem
u = ufl.TrialFunction(V)
v = ufl.TestFunction(V)
x = ufl.SpatialCoordinate(mesh)
f = 10 * ufl.exp(-((x[0] - 0.5)**2 + (x[1] - 0.5)**2) / 0.02)
g = ufl.sin(5 * x[0])
a = inner(grad(u), grad(v)) * dx
L = inner(f, v) * dx + inner(g, v) * ds
Now, we have specified the variational forms and can consider the
solution of the variational problem. First, we need to define a
Function
u
to
represent the solution. (Upon initialization, it is simply set to the
zero function.) A Function
represents a function living in
a finite element function space. Next, we can call the solve
function with the arguments a == L
,
u
and bc
as follows:
# Compute solution
u = Function(V)
solve(a == L, u, bc, petsc_options={"ksp_type": "preonly", "pc_type": "lu"})
The function u
will be modified during the call to solve. The
default settings for solving a variational problem have been
used. However, the solution process can be controlled in much more
detail if desired.
A Function
can be
manipulated in various ways, in particular, it can be plotted and
saved to file. Here, we output the solution to an XDMF
file
for later visualization and also plot it using
the plot
command:
# Save solution in XDMF format
with XDMFFile(MPI.COMM_WORLD, "poisson.xdmf", "w") as file:
file.write_mesh(mesh)
file.write_function(u)
# Update ghost entries and plot
u.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD)
dolfinx.plotting.plot(u)
plt.show()