# Finite element basis functions¶

The specific basis functions exemplified in the section Approximation of functions are in general nonzero on the entire domain
\(\Omega\), as can be seen in Figure A function resulting from a weighted sum of two sine basis functions,
where we plot two sinusoidal basis functions \(\psi_0(x)=\sin\frac{1}{2}\pi x\) and
\(\psi_1(x)=\sin 2\pi x\) together with the sum
\(u(x)=4\psi_0(x) - \frac{1}{2}\psi_1(x)\). We shall
now turn our attention to basis functions that have *compact support*,
meaning that they are nonzero on a small portion of
\(\Omega\) only. Moreover, we shall restrict the functions to be *piecewise
polynomials*. This means that the domain is split into subdomains and
each basis function is a polynomial on one or more of these
subdomains, see Figure
A function resulting from a weighted sum of three local piecewise linear (hat) functions for a sketch involving locally defined
hat functions that make \(u=\sum_jc_j{\psi}_j\) piecewise linear. At
the boundaries between subdomains, one normally just forces continuity of
\(u\), so that when connecting two polynomials from two
subdomains, the derivative becomes discontinuous. This type
of basis functions is fundamental in the finite element method.
(One may wonder why continuity of derivatives is not desired, and it
is, but it turns out to be mathematically challenging
in 2D and 3D, and it is not strictly needed.)

We first introduce the concepts of elements and nodes in a simplistic fashion, as often met in the literature. Later, we shall generalize the concept of an element, which is a necessary step before treating a wider class of approximations within the family of finite element methods. The generalization is also compatible with the concepts used in the FEniCS finite element software.

## Elements and nodes¶

Let \(u\) and \(f\) be defined on an interval \(\Omega\). We divide \(\Omega\) into \(N_e\) non-overlapping subintervals \(\Omega^{(e)}\), \(e=0,\ldots,N_e-1\):

We shall for now
refer to \(\Omega^{(e)}\) as an *element*, identified by the unique number \(e\).
On each element we introduce a set of points called *nodes*.
For now we assume that the nodes are uniformly spaced throughout the
element and that the boundary points of the elements are also nodes.
The nodes are given numbers both within an element and in the global
domain. These are
referred to as *local* and *global* node numbers, respectively.
Local nodes are numbered with an index \(r=0,\ldots,d\), while the
\(N_n\) global nodes are numbered as \(i=0,\ldots,N_n-1\).
Figure Finite element mesh with 5 elements and 6 nodes shows
nodes as small circular
disks and element boundaries as small vertical lines.
Global node numbers appear under the nodes, but local node numbers
are not shown. Since there are two nodes in each elements, the local
nodes are numbered 0 (left) and 1 (right) in each element.

Nodes and elements uniquely define a *finite element mesh*, which is our
discrete representation of the domain in the computations.
A common special case is that of a *uniformly partitioned mesh* where
each element has the same length and the distance between nodes is constant.
Figure Finite element mesh with 5 elements and 6 nodes shows
an example on a uniformly partitioned mesh. The strength of the finite
element method (in contrast to the
finite difference method)
is that it is equally easy to work with a non-uniformly
partitioned mesh as a uniformly partitioned one.

### Example¶

On \(\Omega =[0,1]\) we may introduce two elements, \(\Omega^{(0)}=[0,0.4]\) and \(\Omega^{(1)}=[0.4,1]\). Furthermore, let us introduce three nodes per element, equally spaced within each element. Figure Finite element mesh with 2 elements and 5 nodes shows the mesh with \(N_e=2\) elements and \(N_n=2N_e+1=5\) nodes. A node’s coordinate is denoted by \(x_i\), where \(i\) is either a global node number or a local one. In the latter case we also need to know the element number to uniquely define the node.

The three nodes in element number 0 are \(x_0=0\), \(x_1=0.2\), and \(x_2=0.4\). The local and global node numbers are here equal. In element number 1, we have the local nodes \(x_0=0.4\), \(x_1=0.7\), and \(x_2=1\) and the corresponding global nodes \(x_2=0.4\), \(x_3=0.7\), and \(x_4=1\). Note that the global node \(x_2=0.4\) is shared by the two elements.

For the purpose of implementation, we introduce two lists or arrays:
`nodes`

for storing the coordinates of the nodes, with the
global node numbers as indices, and `elements`

for holding
the global node numbers in each element. By defining `elements`

as a
list of lists, where each sublist contains the global node numbers of
one particular element, the indices of each sublist will correspond to
local node numbers for that element.
The `nodes`

and `elements`

lists for the sample mesh
above take the form

```
nodes = [0, 0.2, 0.4, 0.7, 1]
elements = [[0, 1, 2], [2, 3, 4]]
```

Looking up the coordinate of, e.g., local node number 2 in element 1,
is done by `nodes[elements[1][2]]`

(recall that nodes and
elements start their numbering at 0). The corresponding global node number
is 4, so we could alternatively look up the coordinate as `nodes[4]`

.

The numbering of elements and nodes does not need to be regular. Figure Example on irregular numbering of elements and nodes shows and example corresponding to

```
nodes = [1.5, 5.5, 4.2, 0.3, 2.2, 3.1]
elements = [[2, 1], [4, 5], [0, 4], [3, 0], [5, 2]]
```

## The basis functions¶

### Construction principles¶

Finite element basis functions are in this text recognized by the notation \({\varphi}_i(x)\), where the index (now in the beginning) corresponds to a global node number. Since \({\varphi}_i\) is the symbol for basis functions in general in this text, the particular choice of finite element basis functions means that we take \({\psi}_i = {\varphi}_i\).

Let \(i\) be the global node number corresponding to local node \(r\) in
element number \(e\) with \(d+1\) local nodes. We distinguish between
*internal* nodes in an element and *shared* nodes. The latter are
nodes that are shared with the neighboring elements.
The finite element basis functions \({\varphi}_i\)
are now defined as follows.

- For an internal node, with global number \(i\) and local number \(r\), take \({\varphi}_i(x)\) to be the Lagrange polynomial that is 1 at the local node \(r\) and zero at all other nodes in the element. The degree of the polynomial is \(d\), according to (55). On all other elements, \({\varphi}_i=0\).
- For a shared node, let \({\varphi}_i\) be made up of the Lagrange polynomial on this element that is 1 at node \(i\), combined with the Lagrange polynomial over the neighboring element that is also 1 at node \(i\). On all other elements, \({\varphi}_i=0\).

A visual impression of three such basis functions is given in Figure Illustration of the piecewise quadratic basis functions associated with nodes in an element. The domain \(\Omega = [0,1]\) is divided into four equal-sized elements, each having three local nodes. The element boundaries are marked by vertical dashed lines and the nodes by small circles. The function \({\varphi}_2(x)\) is composed of a quadratic Lagrange polynomial over element 0 and 1, \({\varphi}_3(x)\) corresponds to an internal node in element 1 and is therefore nonzero on this element only, while \({\varphi}_4(x)\) is like \({\varphi}_2(x)\) composed to two Lagrange polynomials over two elements. Also observe that the basis function \({\varphi}_i\) is zero at all nodes, except at global node number \(i\). We also remark that the shape of a basis function over an element is completely determined by the coordinates of the local nodes in the element.

### Properties of \({\varphi}_i\)¶

The construction of basis functions according to the principles above lead to two important properties of \({\varphi}_i(x)\). First,

when \(x_{j}\) is a node in the mesh with global node number \(j\). The result \({\varphi}_i(x_{j}) =\delta_{ij}\) arises because the Lagrange polynomials are constructed to have exactly this property. The property also implies a convenient interpretation of \(c_i\) as the value of \(u\) at node \(i\). To show this, we expand \(u\) in the usual way as \(\sum_jc_j{\psi}_j\) and choose \({\psi}_i = {\varphi}_i\):

Because of this interpretation, the coefficient \(c_i\) is by many named \(u_i\) or \(U_i\).

Second, \({\varphi}_i(x)\) is mostly zero throughout the domain:

- \({\varphi}_i(x) \neq 0\) only on those elements that contain global node \(i\),
- \({\varphi}_i(x){\varphi}_j(x) \neq 0\) if and only if \(i\) and \(j\) are global node numbers in the same element.

Since \(A_{i,j}\) is the integral of
\({\varphi}_i{\varphi}_j\) it means that
*most of the elements in the coefficient matrix will be zero*.
We will come back to these properties and use
them actively in computations to save memory and CPU time.

In our example so far, each element has \(d+1\) nodes, resulting in local Lagrange polynomials of degree \(d\) (according to the section Lagrange polynomials), but it is not a requirement to have the same \(d\) value in each element.

## Example on piecewise quadratic finite element functions¶

Let us set up the `nodes`

and `elements`

lists corresponding to the
mesh implied by Figure Illustration of the piecewise quadratic basis functions associated with nodes in an element.
Figure Sketch of mesh with 4 elements and 3 nodes per element sketches the mesh and the
numbering. We have

```
nodes = [0, 0.125, 0.25, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0]
elements = [[0, 1, 2], [2, 3, 4], [4, 5, 6], [6, 7, 8]]
```

Let us explain in mathematically how the basis functions are constructed according to the principles. Consider element number 1 in Figure Sketch of mesh with 4 elements and 3 nodes per element, \(\Omega^{(1)}=[0.25, 0.5]\), with local nodes 0, 1, and 2 corresponding to global nodes 2, 3, and 4. The coordinates of these nodes are \(0.25\), \(0.375\), and \(0.5\), respectively. We define three Lagrange polynomials on this element:

- The polynomial that is 1 at local node 1 (global node 3) makes up the basis function \({\varphi}_3(x)\) over this element, with \({\varphi}_3(x)=0\) outside the element.
- The polynomial that is 1 at local node 0 (global node 2) is the “right part” of the global basis function \({\varphi}_2(x)\). The “left part” of \({\varphi}_2(x)\) consists of a Lagrange polynomial associated with local node 2 in the neighboring element \(\Omega^{(0)}=[0, 0.25]\).
- Finally, the polynomial that is 1 at local node 2 (global node 4) is the “left part” of the global basis function \({\varphi}_4(x)\). The “right part” comes from the Lagrange polynomial that is 1 at local node 0 in the neighboring element \(\Omega^{(2)}=[0.5, 0.75]\).

The specific mathematical form of the polynomials *over element* 1 is
given by the formula (55):

As mentioned earlier, any global basis function \({\varphi}_i(x)\) is zero on elements that do not contain the node with global node number \(i\).

The other global functions associated with internal nodes, \({\varphi}_1\), \({\varphi}_5\), and \({\varphi}_7\), are all of the same shape as the drawn \({\varphi}_3\) in Figure Illustration of the piecewise quadratic basis functions associated with nodes in an element, while the global basis functions associated with shared nodes have the same shape as shown \({\varphi}_2\) and \({\varphi}_4\). If the elements were of different length, the basis functions would be stretched according to the element size and hence be different.

## Example on piecewise linear finite element functions¶

Figure Illustration of the piecewise linear basis functions associated with nodes in an element shows piecewise linear basis functions (\(d=1\)). These are mathematically simpler than the quadratic functions in the previous section, and one would therefore think that it is easier to understand the linear functions first. However, linear basis functions do not involve internal nodes and are therefore a special case of the general situation. That is why we think it is better to understand the construction of quadratic functions first, which easily generalize to any \(d > 2\), and then look at the special case \(d=1\).

We have the same four elements on \(\Omega = [0,1]\). Now there are no internal nodes in the elements so that all basis functions are associated with shared nodes and hence made up of two Lagrange polynomials, one from each of the two neighboring elements. For example, \({\varphi}_1(x)\) results from the Lagrange polynomial in element 0 that is 1 at local node 1 and 0 at local node 0, combined with the Lagrange polynomial in element 1 that is 1 at local node 0 and 0 at local node 1. The other basis functions are constructed similarly.

Explicit mathematical formulas are needed for \({\varphi}_i(x)\) in computations. In the piecewise linear case, the formula (55) leads to

Here, \(x_{j}\), \(j=i-1,i,i+1\), denotes the coordinate of node \(j\). For elements of equal length \(h\) the formulas can be simplified to

## Example on piecewise cubic finite element basis functions¶

Piecewise cubic basis functions can be defined by introducing four nodes per element. Figure Illustration of the piecewise cubic basis functions associated with nodes in an element shows examples on \({\varphi}_i(x)\), \(i=3,4,5,6\), associated with element number 1. Note that \({\varphi}_4\) and \({\varphi}_5\) are nonzero on element number 1, while \({\varphi}_3\) and \({\varphi}_6\) are made up of Lagrange polynomials on two neighboring elements.

We see that all the piecewise linear basis functions have the same
“hat” shape. They are naturally referred to as *hat functions*,
also called *chapeau functions*.
The piecewise quadratic functions in Figure Illustration of the piecewise quadratic basis functions associated with nodes in an element
are seen to be of two types. “Rounded hats” associated with internal
nodes in the elements and some more “sombrero” shaped hats associated
with element boundary nodes. Higher-order basis functions also have
hat-like shapes, but the functions have pronounced oscillations in addition,
as illustrated in Figure Illustration of the piecewise cubic basis functions associated with nodes in an element.

A common terminology is to speak about *linear elements* as
elements with two local nodes associated with
piecewise linear basis functions. Similarly, *quadratic elements* and
*cubic elements* refer to piecewise quadratic or cubic functions
over elements with three or four local nodes, respectively.
Alternative names, frequently used in the following, are P1 elements for linear
elements, P2 for quadratic elements, and so forth: Pd signifies
degree \(d\) of the polynomial basis functions.

## Calculating the linear system¶

The elements in the coefficient matrix and right-hand side are given by the formulas (27) and (28), but now the choice of \({\psi}_i\) is \({\varphi}_i\). Consider P1 elements where \({\varphi}_i(x)\) is piecewise linear. Nodes and elements numbered consecutively from left to right in a uniformly partitioned mesh imply the nodes

and the elements

We have in this case \(N_e\) elements and \(N_n=N_e+1\) nodes. The parameter \(N\) denotes the number of unknowns in the expansion for \(u\), and with the P1 elements, \(N=N_n-1\). The domain is \(\Omega=[x_{0},x_{N}]\). The formula for \({\varphi}_i(x)\) is given by (63) and a graphical illustration is provided in Figures Illustration of the piecewise linear basis functions associated with nodes in an element and Illustration of two neighboring linear (hat) functions with general node numbers.

### Calculating specific matrix entries¶

Let us calculate the specific matrix entry \(A_{2,3} = \int_\Omega {\varphi}_2{\varphi}_3{\, \mathrm{d}x}\). Figure Illustration of the piecewise linear basis functions corresponding to global node 2 and 3 shows what \({\varphi}_2\) and \({\varphi}_3\) look like. We realize from this figure that the product \({\varphi}_2{\varphi}_3\neq 0\) only over element 2, which contains node 2 and 3. The particular formulas for \({\varphi}_{2}(x)\) and \({\varphi}_3(x)\) on \([x_{2},x_{3}]\) are found from (63). The function \({\varphi}_3\) has positive slope over \([x_{2},x_{3}]\) and corresponds to the interval \([x_{i-1},x_{i}]\) in (63). With \(i=3\) we get

while \({\varphi}_2(x)\) has negative slope over \([x_{2},x_{3}]\) and corresponds to setting \(i=2\) in (63),

We can now easily integrate,

The diagonal entry in the coefficient matrix becomes

The entry \(A_{2,1}\) has an integral that is geometrically similar to the situation in Figure Illustration of the piecewise linear basis functions corresponding to global node 2 and 3, so we get \(A_{2,1}=h/6\).

### Calculating a general row in the matrix¶

We can now generalize the calculation of matrix entries to a general row number \(i\). The entry \(A_{i,i-1}=\int_\Omega{\varphi}_i{\varphi}_{i-1}{\, \mathrm{d}x}\) involves hat functions as depicted in Figure Illustration of two neighboring linear (hat) functions with general node numbers. Since the integral is geometrically identical to the situation with specific nodes 2 and 3, we realize that \(A_{i,i-1}=A_{i,i+1}=h/6\) and \(A_{i,i}=2h/3\). However, we can compute the integral directly too:

The particular formulas for \({\varphi}_{i-1}(x)\) and \({\varphi}_i(x)\) on \([x_{i-1},x_{i}]\) are found from (63): \({\varphi}_i\) is the linear function with positive slope, corresponding to the interval \([x_{i-1},x_{i}]\) in (63), while \(\phi_{i-1}\) has a negative slope so the definition in interval \([x_{i},x_{i+1}]\) in (63) must be used.

The first and last row of the coefficient matrix lead to slightly different integrals:

Similarly, \(A_{N,N}\) involves an integral over only one element and hence equals \(h/3\).

The general formula for \(b_i\), see Figure Right-hand side integral with the product of a basis function and the given function to approximate, is now easy to set up

We need a specific \(f(x)\) function to compute these integrals. With \(f(x)=x(1-x)\) and two equal-sized elements in \(\Omega=[0,1]\), one gets

The solution becomes

The resulting function

is displayed in Figure Least squares approximation of a parabola using 2 (left) and 4 (right) P1 elements (left). Doubling the number of elements to four leads to the improved approximation in the right part of Figure Least squares approximation of a parabola using 2 (left) and 4 (right) P1 elements.

## Assembly of elementwise computations¶

Our integral computations so far have been straightforward. However, with higher-degree polynomials and in higher dimensions (2D and 3D), integrating in the physical domain gets increasingly complicated. Instead, integrating over one element at a time, and transforming each element to a common standardized geometry in a new reference coordinate system, is technically easier. Almost all computer codes employ a finite element algorithm that calculates the linear system by integrating over one element at a time. We shall therefore explain this algorithm next. The amount of details might be overwhelming during a first reading, but once all those details are done right, one has a general finite element algorithm that can be applied to all sorts of elements, in any space dimension, no matter how geometrically complicated the domain is.

### The element matrix¶

We start by splitting the integral over \(\Omega\) into a sum of contributions from each element:

Now, \(A^{(e)}_{i,j}\neq 0\), if and only if, \(i\) and \(j\) are nodes in element
\(e\) (look at Figure Illustration of two neighboring linear (hat) functions with general node numbers to realize this
property, but the result also holds for all types of elements).
Introduce \(i=q(e,r)\) as the mapping of local node number \(r\) in element
\(e\) to the global node number \(i\). This is just a short mathematical notation
for the expression `i=elements[e][r]`

in a program.
Let \(r\) and \(s\) be the local node numbers corresponding to the global
node numbers \(i=q(e,r)\) and
\(j=q(e,s)\). With \(d\) nodes per element, all the nonzero matrix entries
in \(A^{(e)}_{i,j}\) arise from the integrals involving basis functions with
indices corresponding to the global node numbers in element number \(e\):

These contributions can be collected in a \((d+1)\times (d+1)\) matrix known as
the *element matrix*. Let \({I_d}=\{0,\ldots,d\}\) be the valid indices
of \(r\) and \(s\).
We introduce the notation

for the element matrix. For P1 elements (\(d=1\)) we have

while P2 elements have a \(3\times 3\) element matrix:

### Assembly of element matrices¶

Given the numbers \(\tilde A^{(e)}_{r,s}\), we should, according to (66), add the contributions to the global coefficient matrix by

This process of adding in elementwise contributions to the global matrix
is called *finite element assembly* or simply *assembly*.

Figure Illustration of matrix assembly: regularly numbered P1 elements illustrates how element matrices for elements with two nodes are added into the global matrix. More specifically, the figure shows how the element matrix associated with elements 1 and 2 assembled, assuming that global nodes are numbered from left to right in the domain. With regularly numbered P3 elements, where the element matrices have size \(4\times 4\), the assembly of elements 1 and 2 are sketched in Figure Illustration of matrix assembly: regularly numbered P3 elements.

### Assembly of irregularly numbered elements and nodes¶

After assembly of element matrices corresponding to regularly numbered elements
and nodes are understood, it is wise to study the assembly process for
irregularly numbered elements and nodes. Figure Example on irregular numbering of elements and nodes shows a mesh where the `elements`

array, or \(q(e,r)\)
mapping in mathematical notation, is given as

```
elements = [[2, 1], [4, 5], [0, 4], [3, 0], [5, 2]]
```

The associated assembly of element matrices 1 and 2 is sketched in Figure Illustration of matrix assembly: irregularly numbered P1 elements.

We have created animations to illustrate the assembly of P1 and P3 elements with regular numbering as well as P1 elements with irregular numbering. The reader is encouraged to develop a “geometric” understanding of how element matrix entries are added to the global matrix. This understanding is crucial for hand computations with the finite element method.

### The element vector¶

The right-hand side of the linear system is also computed elementwise:

We observe that
\(b_i^{(e)}\neq 0\) if and only if global node \(i\) is a node in element \(e\)
(look at Figure Right-hand side integral with the product of a basis function and the given function to approximate to realize this property).
With \(d\) nodes per element we can collect the \(d+1\) nonzero contributions
\(b_i^{(e)}\), for \(i=q(e,r)\), \(r\in{I_d}\), in an *element vector*

These contributions are added to the global right-hand side by an assembly process similar to that for the element matrices:

## Mapping to a reference element¶

Instead of computing the integrals

over some element \(\Omega^{(e)} = [x_L, x_R]\) in the physical coordinate system, it turns out that it is considerably easier and more convenient to map the element domain \([x_L, x_R]\) to a standardized reference element domain \([-1,1]\) and compute all integrals over the same domain \([-1,1]\). We have now introduced \(x_L\) and \(x_R\) as the left and right boundary points of an arbitrary element. With a natural, regular numbering of nodes and elements from left to right through the domain, we have \(x_L=x_{e}\) and \(x_R=x_{e+1}\) for P1 elements.

### The coordinate transformation¶

Let \(X\in [-1,1]\) be the coordinate in the reference element. A linear mapping, also known as an affine mapping, from \(X\) to \(x\) can be written

This relation can alternatively be expressed as

where we have introduced the element midpoint \(x_m=(x_L+x_R)/2\) and the element length \(h=x_R-x_L\).

### Formulas for the element matrix and vector entries¶

Integrating on the reference element is a matter of just changing the integration variable from \(x\) to \(X\). Let

be the basis function associated with local node number \(r\) in the reference element. Switching from \(x\) to \(X\) as integration variable, using the rules from calculus, results in

In 2D and 3D, \({\, \mathrm{d}x}\) is transformed to \(\hbox{det} J{\, \mathrm{d}X}\), where \(J\) is the Jacobian of the mapping from \(x\) to \(X\). In 1D, \(\hbox{det} J{\, \mathrm{d}X} = {\, \mathrm{d}x}/{\, \mathrm{d}X} = h/2\).To obtain a uniform notation for 1D, 2D, and 3D problems we therefore replace \({\, \mathrm{d}x}/{\, \mathrm{d}X}\) by \(\det J\) already now. The integration over the reference element is then written as

The corresponding formula for the element vector entries becomes

Why reference elements

The great advantage of using reference elements is that the formulas for the basis functions, \({\tilde{\varphi}}_r(X)\), are the same for all elements and independent of the element geometry (length and location in the mesh). The geometric information is “factored out” in the simple mapping formula and the associated \(\det J\) quantity. Also, the integration domain is the same for all elements. All these features contribute to simplify computer codes and make them more general.

### Formulas for local basis functions¶

The \({\tilde{\varphi}}_r(x)\) functions are simply the Lagrange polynomials defined through the local nodes in the reference element. For \(d=1\) and two nodes per element, we have the linear Lagrange polynomials

Quadratic polynomials, \(d=2\), have the formulas

In general,

where \(X_{(0)},\ldots,X_{(d)}\) are the coordinates of the local nodes in the reference element. These are normally uniformly spaced: \(X_{(r)} = -1 + 2r/d\), \(r\in{I_d}\).

## Example: Integration over a reference element¶

To illustrate the concepts from the previous section in a specific example, we now consider calculation of the element matrix and vector for a specific choice of \(d\) and \(f(x)\). A simple choice is \(d=1\) (P1 elements) and \(f(x)=x(1-x)\) on \(\Omega =[0,1]\). We have the general expressions (74) and (75) for \(\tilde A^{(e)}_{r,s}\) and \(\tilde b^{(e)}_{r}\). Writing these out for the choices (76) and (77), and using that \(\det J = h/2\), we can do the following calculations of the element matrix entries:

The corresponding entries in the element vector becomes

In the last two expressions we have used the element midpoint \(x_m\).

Integration of lower-degree polynomials above is tedious,
and higher-degree polynomials involve much more algebra, but `sympy`

may help. For example, we can easily calculate
(82),
(83),
and (86) by

```
>>> import sympy as sym
>>> x, x_m, h, X = sym.symbols('x x_m h X')
>>> sym.integrate(h/8*(1-X)**2, (X, -1, 1))
h/3
>>> sym.integrate(h/8*(1+X)*(1-X), (X, -1, 1))
h/6
>>> x = x_m + h/2*X
>>> b_0 = sym.integrate(h/4*x*(1-x)*(1-X), (X, -1, 1))
>>> print b_0
-h**3/24 + h**2*x_m/6 - h**2/12 - h*x_m**2/2 + h*x_m/2
```