Defining variational problems

Firedrake uses a high-level language, UFL, to describe variational problems. To do this, we need a number of pieces. We need a representation of the domain we’re solving the PDE on: Firedrake uses a Mesh() for this. On top of this mesh, we build FunctionSpaces which define the space in which the solutions to our equation live. Finally we define Functions in those function spaces to actually hold the solutions.

Constructing meshes

Firedrake can read meshes in Gmsh, triangle, CGNS, and Exodus formats. To build a mesh one uses the Mesh() constructor, passing the name of the file as an argument, which see for more details. The mesh type is determined by the file extension, for example if the provided filename is coastline.msh the mesh is assumed to be in Gmsh format, in which case you can construct a mesh object like so:

coastline = Mesh("coastline.msh")

This works in both serial and parallel, Firedrake takes care of decomposing the mesh among processors transparently.

Reordering meshes for better performance

Most mesh generators produce badly numbered meshes (with bad data locality) which can reduce the performance of assembling and solving finite element problems. By default then, Firedrake reorders input meshes to improve data locality by performing reverse Cuthill-McKee reordering on the adjacency matrix of the input mesh. If you know your mesh has a good numbering (perhaps your mesh generator uses space filling curves to number entities) then you can switch off this reordering by passing reorder=False to the appropriate Mesh() constructor. You can control Firedrake’s default behaviour in reordering meshes with the "reorder_meshes" parameter. For example, to turn off mesh reordering globally:

from firedrake import *
parameters["reorder_meshes"] = False

The parameter passed in to the mesh constructor overrides this default value.


Firedrake numbers degrees of freedom in a function space by visiting each cell in order and performing a depth first numbering of all degrees of freedom on that cell. Hence, if your mesh has a good numbering, the degrees of freedom will too.

Utility mesh functions

As well as offering the ability to read mesh information from a file, Firedrake also provides a number of built in mesh types for a number of standard shapes. 1-dimensional intervals may be constructed with IntervalMesh(); 2-dimensional rectangles with RectangleMesh(); and 3-dimensional boxes with BoxMesh(). There are also more specific constructors (for example to build unit square meshes). See utility_meshes for full details.

Immersed manifolds

In addition to the simple meshes described above, Firedrake also has support for solving problems on orientable immersed manifolds. That is, meshes in which the entities are immersed in a higher dimensional space. For example, the surface of a sphere in 3D.

If your mesh is such an immersed manifold, you need to tell Firedrake that the geometric dimension of the coordinate field (defining where the points in mesh are) is not the same as the topological dimension of the mesh entities. This is done by passing an optional second argument to the mesh constructor which specifies the geometric dimension. For example, for the surface of a sphere embedded in 3D we use:

sphere_mesh = Mesh('sphere_mesh.node', dim=3)

Firedrake provides utility meshes for the surfaces of spheres immersed in 3D that are approximated using an icosahedral mesh. You can either build a mesh of the unit sphere with UnitIcosahedralSphereMesh(), or a mesh of a sphere with specified radius using IcosahedralSphereMesh(). The meshes are constructed by recursively refining a regular icosahedron, you can specify the refinement level by passing a non-zero refinement_level to the constructor. For example, to build a sphere mesh that approximates the surface of the Earth (with a radius of 6371 km) that has subdivided the original icosahedron 7 times we would write:

earth = IcosahedralSphereMesh(radius=6371, refinement_level=7)

Ensuring consistent cell orientations

Variational forms that include particular function spaces (those requiring a contravariant Piola transform), require information about the orientation of the cells. For normal meshes, this can be deduced automatically. However, when using immersed meshes, Firedrake needs extra information to calculate the orientation of each cell relative to some global orientation. This is used by Firedrake to ensure that the cell normal on, say, the surface of a sphere, uniformly points outwards. To do this, after constructing an immersed mesh, we must initialise the cell orientation information. This is carried out with the function init_cell_orientations(), which takes a UFL expression or an Expression used to produce the reference normal direction. For example, on the sphere mesh of the earth defined above we can initialise the cell orientations relative to vector pointing out from the origin:


However, a more complicated expression would be needed to initialise the cell orientations on a toroidal mesh.

Semi-structured extruded meshes

Firedrake has special support for solving PDEs on high-aspect ratio domains, such as in the ocean or atmosphere, where the numerics dictate that the “short” dimension should be structured. These are termed extruded meshes and have a separate section in the manual.

Building function spaces

Now that we have a mesh of our domain, we need to build the function spaces the solution to our PDE will live in, along with the spaces for the trial and test functions. To do so, we use the FunctionSpace() constructor. This is the only way to obtain a function space for a scalar variable, such as pressure, which has a single value at each point in the domain.

To construct a function space, you must specify its family and polynomial degree. To build a scalar-valued function space of continuous piecewise-cubic polynomials, we write:

V = FunctionSpace(mesh, "Lagrange", 3)

There are three main routes to obtaining a function space for a vector-valued variable such as velocity. Firstly, you can pass the FunctionSpace() constructor a natively vector-valued family such as "Raviart-Thomas". Secondly, you may use the VectorFunctionSpace() constructor with a scalar-valued family, which gives a vector-valued space where each component is identical to the appropriate scalar-valued FunctionSpace. Thirdly, you can create a VectorElement directly (which is itself vector-valued and pass that to the FunctionSpace() constructor).

To build a vector-valued function space using the lowest-order Raviart-Thomas elements, we write

V = FunctionSpace(mesh, "Raviart-Thomas", 1)

To build a vector-valued function space for which each component is a discontinuous piecewise-quadratic polynomial, we can write either

V = VectorFunctionSpace(mesh, "Discontinuous Lagrange", 2)


Vele = VectorElement("Discontinuous Lagrange", cell=mesh.ufl_cell(), degree=2)
V = FunctionSpace(mesh, Vele)

Firedrake supports the use of all function spaces generated by FIAT.

Advanced usage of VectorFunctionSpace

By default, the number of components of a VectorFunctionSpace() is the geometric dimension of the mesh (e.g. 3, if the mesh is 3D). However, sometimes we might want the number of components in the vector to differ from the geometric dimension of the mesh. We can do this by passing a value for the dim argument to the VectorFunctionSpace() constructor. For example, if we wanted a vector-valued function space on the surface of a unit sphere mesh with only 2 components, we might write:

mesh = UnitIcosahedralSphereMesh(refinement_level=3)
V = VectorFunctionSpace(mesh, "Lagrange", 1, dim=2)

Mixed function spaces

Many PDEs are posed in terms of multiple, coupled, variables. The variational problem for such a PDE uses a so-called mixed function space. In Firedrake, this is represented by a MixedFunctionSpace. We can either build such a space by invoking the constructor directly, or, more readably, by taking existing function spaces and multiplying them together using the * operator. For example:

V = FunctionSpace(mesh, 'RT', 1)
Q = FunctionSpace(mesh, 'DG', 0)
W = V*Q

is equivalent to:

V = FunctionSpace(mesh, 'RT', 1)
Q = FunctionSpace(mesh, 'DG', 0)
W = MixedFunctionSpace([V, Q])

Function spaces on extruded meshes

On extruded meshes, we build function spaces by taking a tensor product of the base (“horizontal”) space and the extruded (“vertical”) space. Firedrake allows us to separately choose the horizontal and vertical spaces when building a function space on an extruded mesh. We refer the reader to the manual section on extrusion for details.

Expressing a variational problem

Firedrake uses the UFL language to express variational problems. For complete documentation, we refer the reader to the UFL package documentation and the description of the language in TOMS. We present a brief overview of the syntax here, for a more didactic introduction, we refer the reader to the Firedrake tutorial examples.

Building test and trial spaces

Now that we have function spaces that our solution will live in, the next step is to actually write down the variational form of the problem we wish to solve. To do this, we will need a test function in an appropriate space along with a function to hold the solution and perhaps a trial function. Test functions are obtained via a call to TestFunction, trial functions via TrialFunction and functions with Function. The former two are purely symbolic objects, the latter contains storage for the coefficients of the basis functions in the function space. We use them as follows:

u = TrialFunction(V)
v = TestFunction(V)
f = Function(V)


A newly allocated Function has coefficients which are all zero.

If V above were a MixedFunctionSpace, the test and trial functions we obtain are for the combined mixed space. Often, we would like to have test and trial functions for the subspaces of the mixed space. We can do this by asking for TrialFunctions and TestFunctions, which return an ordered tuple of test and trial functions for the underlying spaces. For example, if we write:

V = FunctionSpace(mesh, 'RT', 1)
Q = FunctionSpace(mesh, 'DG', 0)
W = V * Q

u, p = TrialFunctions(W)
v, q = TestFunctions(W)

then u and v will be, respectively, trial and test functions for V, while p and q will be trial and test functions for Q.


If we intend to build a variational problem on a mixed space, we cannot build the individual test and trial functions on the function spaces that were used to construct the mixed space directly. The functions that we build must “know” that they come from a mixed space or else Firedrake will not be able to assemble the correct system of equations.

A first variational form

With our test and trial functions defined, we can write down our first variational form. Let us consider solving the identity equation:

\[u = f \quad \mathrm{on} \, \Omega\]

where \(\Omega\) is the unit square, using piecewise linear polynomials for our solution. We start with a mesh and build a function space on it:

mesh = UnitSquareMesh(10, 10)
V = FunctionSpace(mesh, "CG", 1)

now we need a test function, and since u is unknown, a trial function:

u = TrialFunction(V)
v = TestFunction(V)

finally we need a function to hold the right hand side \(f\) which we will populate with the x component of the coordinate field.

f = Function(V)
x = SpatialCoordinate(mesh)

For details on how interpolate() works, see the appropriate section in the manual. The variational problem is to find \(u \in V\) such that

\[\int_\Omega \! u v \, \mathrm{d}x = \int_\Omega \! f v \, \mathrm{d}x \quad \forall v \in V\]

we define the variational problem in UFL with:

a = u*v*dx
L = f*v*dx

Where the dx indicates that the integration should be carried out over the cells of the mesh. UFL can also express integrals over the boundary of the domain, using ds, and the interior facets of the domain, using dS.

How to solve such variational problems is the subject of the next section, but for completeness we show how to do it here. First we define a function to hold the solution

s = Function(V)

and call solve() to solve the variational problem:

solve(a == L, s)

Forms with constant coefficients

Many PDEs will contain values that are constant over the whole mesh, but may vary in time. For example, a time-varying diffusivity, or a time-dependent forcing function. Although you can create a new form for each new value of this constant, this will not be efficient, since Firedrake must generate new code each time the value changes. A better option is to use a Constant coefficient. This object behaves exactly like a Function, except that it has a single value over the whole mesh. One may assign a new value to the Constant using the assign() method. As an example, let us consider a form which contains a time varying constant which we wish to assemble in a time loop. We can use a Constant to do this:

t = 0
dt = 0.1
from math import exp
c = Constant(exp(-t))
# Exponentially decaying RHS
L = f*v*c*dx
while t < tend:
    solve(a == L, ...)
    t += dt


Although UFL supports computing the derivative of a form with respect to a Constant, the resulting form will have an unknown in the reals, which is currently unsupported by Firedrake.

Incorporating boundary conditions

Boundary conditions enter the variational problem in one of two ways. Natural (often termed Neumann or weak) boundary conditions, which prescribe values of the derivative of the solution, are incorporated into the variational form. Essential (often termed Dirichlet or strong) boundary conditions, which prescribe values of the solution, become prescriptions on the function space. In Firedrake, the former are naturally expressed as part of the formulation of the variational problem, the latter are represented as DirichletBC objects and are applied when solving the variational problem. Construction of such a strong boundary condition requires a function space (to impose the boundary condition in), a value and a subdomain to apply the boundary condition over:

bc = DirichletBC(V, value, subdomain_id)

The subdomain_id is an integer indicating which section of the mesh the boundary condition should be applied to. The subdomain ids for the various utility meshes are described in their respective constructor documentation. For externally generated meshes, Firedrake just uses whichever ids the mesh generator provided. The value may be either a scalar, or more generally an Expression, Function or Constant of the appropriate shape. You may also supply an iterable of literal constants:

bc = DirichletBC(V, (1.0, 2.0), 1)

Strong boundary conditions are applied in the solve by passing a list of boundary condition objects:

solve(a == L, bcs=[bc])

See the next section for a more complete description of the interface Firedrake provides to solve PDEs. The details of how Firedrake applies strong boundary conditions are slightly involved and therefore have their own section in the manual.

Specifying conditions on components of a space

When solving a problem defined on either a MixedFunctionSpace or a rank-1 FunctionSpace, it is common to want to specify boundary values for only some of the components. In the former case, this is the only supported method of setting boundary values, the latter also supports setting the value for all components. In both cases, the syntax is the same. When defining the DirichletBC we must index the function space used. For example, to specify that the third component of a VectorFunctionSpace() should take the boundary value 0, we write:

V = VectorFunctionSpace(mesh, ...)
bc = DirichletBC(V.sub(2), Constant(0), boundary_ids)

Note that when indexing a MixedFunctionSpace in this manner, one pulls out the indexed sub-space, rather than a component. For example, to specify the velocity values in a Taylor-Hood discretisation we write:

V = VectorFunctionSpace(mesh, "CG", 2)
P = FunctionSpace(mesh, "CG", 1)
W = V*P

bcv = DirichletBC(W.sub(0), Constant((0, 0)), boundary_ids)

If we only wanted to specify a single component, we would have to index twice. For example, specifying that the x-component of the velocity is zero, using the same function space definitions:

bcv_x = DirichletBC(W.sub(0).sub(0), Constant(0), boundary_ids)


Extruded meshes have full support for indexing MixedFunctionSpaces, but currently do not support indexing on VectorFunctionSpace()s.

Boundary conditions in discontinuous spaces

The default method Firedrake uses to determine where to apply strong boundary conditions is "topological", meaning that nodes topologically associated with a boundary facet will be included. In discontinuous spaces, however, the nodes to be included do not all live on boundary facets, in this case, you should use the "geometric" method for determining boundary condition nodes. In this case, nodes associated with basis functions that do not vanish on the boundary are included. This method can be used to impose strong boundary conditions on discontinuous galerkin spaces, or no-slip conditions on \(H(\textrm{div})\) spaces. To select the method used for determining boundary condition nodes, use the method argument to the DirichletBC constructor. For example, to select geometric boundary node determination we would write:

V = FunctionSpace(mesh, 'DG', 2)
bc = DirichletBC(V, 1.0, subdomain_id, method="geometric")

Time dependent boundary conditions

Imposition of time-dependent boundary conditions can by carried out by modifying the value in the appropriate DirichletBC object. Note that if you use a literal value to initialise the boundary condition object within the timestepping loop, this will necessitate a recompilation of code every time the boundary condition changes. For this reason we either recommend using a Constant if the boundary condition is spatially uniform, or a UFL expression if it has both space and time-dependence. For example, a purely time-varying boundary condition might be implemented as:

c = Constant(sin(t))
bc = DirichletBC(V, c, 1)
while t < T:
    solve(F == 0, bcs=[bc])
    t += dt

If the boundary condition instead has both space and time dependence we can write:

c = Constant(t)
e = sin(x[0]*c)
bc = DirichletBC(V, e, 1)
while t < T:
    solve(F == 0, bcs=[bc])
    t += dt

More complicated forms

UFL is a fully-fledged language for expressing variational problems, and hence has operators for all appropriate vector calculus operations along with special support for discontinuous galerkin methods in the form of symbolic expressions for facet averages and jumps. For an introduction to these concepts we refer the user to the UFL manual as well as the Firedrake tutorials which cover a wider variety of different problems.