Contents
  1. The Boundary Element Method
  2. Weighted Residual Methods
  3. Galerkin Approximations in Two Dimensions
  4. Natural Boundary Conditions
  5. The General Weighted Residual Statement
  6. The BEM for the Laplace and Poisson Equations
  7. Fundamental Solutions
  8. A One-Dimensional Example
  9. Flashcards

The Boundary Element Method

The boundary element method (BEM) is a method for solving differential equations, especially partial differential equations, by discretizing only the boundary of the region. In two dimensions the boundary is a curve; in one dimension the boundary is just the two endpoints of an interval.

Most other numerical methods (finite differences FDM, finite elements FEM, finite volumes FVM, …) discretize the whole region: a grid or a mesh of elements covers the entire domain. The boundary element method discretizes only the boundary, so the resulting linear system is much smaller. This is the main advantage of the method.

Example. Consider the Laplace equation with mixed boundary conditions:

∇2u=0  in Ω,u=u¯  given on Γ1,q=∂u∂n=q¯  given on Γ2,

where ∂Ω=Γ1∪Γ2. On Γ1 the value u is known and q is unknown; on Γ2 the flux q is known and u is unknown.

The steps of the method are:

  1. Transform the PDE in Ω into an integral equation on the boundary ∂Ω.
  2. Discretize the boundary ∂Ω.
  3. Solve the integral equation on ∂Ω by discretizing it.
  4. The result is a linear system A𝐮=𝐠, where 𝐮 is the vector of unknown boundary values.
  5. Use the boundary values to obtain the solution at an interior point.

The tool behind the first step is the weighted residual method (WRM), a general way of finding approximate solutions of differential equations. The next sections develop it on simple examples.


Weighted Residual Methods

The residual

Example. Consider the initial value problem

y′(x)=x−y,0≤x≤1,y(0)=0.

If y is the exact solution, then y′−x+y=0. If y^ is only an approximate solution, the left-hand side is not zero:

ℰ(x)=y^′(x)−x+y^(x).

Definition. Let y^ be an approximate solution of a differential equation written in the form (expression in y)=0. The function ℰ(x) obtained by substituting y^ into the left-hand side is the residual of y^. It is identically zero exactly when y^ solves the equation.

We want the residual to be as small as possible. Several methods make this precise. All of them start with an approximate form for the solution containing unknown coefficients. Here we take

y^(x)=ax2+bx3.

There is no constant term because y(0)=0; there is no linear term either, so every choice of a,b satisfies the initial condition. Then

y^′(x)=2ax+3bx2,ℰ(x)=2ax+3bx2−x+ax2+bx3.

Each method below gives two equations for the two unknowns a and b.

1. The collocation method

Definition. In the collocation method we choose as many points xi in the region as there are unknown coefficients in y^, and require the residual to vanish at those points: ℰ(xi)=0.

Take x1=0.5 and x2=1. Then

ℰ(0.5)=y^′(0.5)−0.5+y^(0.5)=1.25a+0.875b−0.5=0,ℰ(1)=y^′(1)−1+y^(1)=3a+4b−1=0.

Solving these two equations gives

y^(x)=0.474x2−0.105x3,

an approximate solution in the sense of collocation.

If we know the exact solution, we can check the difference. Here y(x)=x−1+e−x, and

|y(1/3)−y^(1/3)|≈10−3,

so at x=1/3, for example, y^ is an approximate solution with accuracy about 10−3.

2. The method of moments

Definition. In the method of moments the weighting functions are the monomials 1,x,x2,x3,…: we require ∫01ℰ(x)xidx=0,i=0,1,2,…, taking as many monomials as there are unknown coefficients.

With two unknowns we use 1 and x:

∫01(2ax+3bx2−x+ax2+bx3)dx=0,∫01(2ax+3bx2−x+ax2+bx3)xdx=0.

These give

1.333a+1.25b−0.5=0,0.9167a+0.95b−0.333=0,

and so

y^(x)=0.483x2−0.115x3,

an approximate solution in the sense of the method of moments.

3. The Galerkin method

Definition. In the Galerkin method the weighting functions are the same functions that are used in the approximation. For y^=ax2+bx3 these are x2 and x3: ∫01ℰ(x)x2dx=0,∫01ℰ(x)x3dx=0.

Solving the resulting system for a,b gives

y^(x)=0.475x2−0.107x3,

the approximate solution in the sense of Galerkin.

4. The subdomain method

Definition. In the subdomain method the region is divided into as many subintervals as there are unknown coefficients, and the integral of the residual over each subinterval is set to zero.

With the subintervals [0,1/2] and [1/2,1]:

∫01/2ℰ(x)dx=0,∫1/21ℰ(x)dx=0,

which gives

y^(x)=0.485x2−0.118x3

in the sense of the subdomain method.

5. The method of least squares

Definition. In the method of least squares the coefficients are chosen to minimize ∫01ℰ2(x)dx, so the derivatives with respect to the coefficients vanish: ∂∂a∫01ℰ2(x)dx=0,∂∂b∫01ℰ2(x)dx=0.

This gives

y^(x)=0.477x2−0.110x3

in the sense of least squares.

Summary. For y′=x−y, y(0)=0 and y^=ax2+bx3:

Method a b
Collocation (x=0.5,1) 0.474 −0.105
Method of moments (1,x) 0.483 −0.115
Galerkin (x2,x3) 0.475 −0.107
Subdomain ([0,12],[12,1]) 0.485 −0.118
Least squares 0.477 −0.110

A boundary value problem

Example. Consider the boundary value problem

ϕ″+ϕ+x=0,0<x<1,ϕ(0)=0,ϕ(1)=0.

Take a cubic polynomial as the approximate solution:

ϕ^(x)=c0+c1x+c2x2+c3x3.

The boundary conditions give ϕ^(0)=0⇒c0=0 and ϕ^(1)=0⇒c1+c2+c3=0. Let c1=a, c2=b−a, c3=−b. Then

ϕ^(x)=x(1−x)(a+bx)=ax(1−x)⏟N1+bx2(1−x)⏟N2.

The functions N1,N2 are the shape functions.

Galerkin method. The residual is

ℰ(x)=ϕ^″+ϕ^+x=x+a(−2+x−x2)+b(2−6x+x2−x3),

and we make it as small as possible in the sense of Galerkin, with the shape functions as weights:

∫01ℰ(x)x(1−x)dx=0,∫01ℰ(x)x2(1−x)dx=0.

This gives the system

(3/103/203/2013/105)(ab)=(1/121/20),a=71369,b=741,

so the approximate solution is

ϕ^(x)=x(1−x)(71369+741x).

The exact solution is ϕ(x)=sin⁡xsin⁡1−x. Comparing:

x ϕ^(x) ϕ(x) difference
0.25 0.0441 0.044014 ≈10−4
0.50 0.0694 0.069747 ≈10−3
0.75 0.0601 0.060056 ≈10−4

So ϕ^ is accurate with a tolerance of 10−3.

Exercise. Solve the differential equation

d2udx2+ku+x=0,0<x<1,k constant,

with u(0)=0 and q=dudx=q¯ given at x=1, using the Galerkin approach and the two-term approximation u^=a1x+a2x2.


Galerkin Approximations in Two Dimensions

Example. Consider the Poisson problem on a rectangle:

−∇2u=c,0≤x≤a,0≤y≤b,∇2=∂2∂x2+∂2∂y2,

u(0,y)=0,u(a,y)=0,u(x,0)=0,u(x,b)=0,

where c≠0. (If c=0, the boundary values are zero and by the maximum principle the only solution is u=0.)

Let

u^(x,y)=αxy(x−a)(y−b),

which satisfies all the boundary conditions. Then

∂u^∂x=α[y(x−a)(y−b)+xy(y−b)],∂2u^∂x2=α[y(y−b)+y(y−b)]=2αy(y−b),∂u^∂y=α[x(x−a)(y−b)+xy(x−a)],∂2u^∂y2=α[x(x−a)+x(x−a)]=2αx(x−a).

Thus the Laplacian of u^ is

∇2u^=2α[y(y−b)+x(x−a)].

Galerkin approach. The weight is the trial function xy(x−a)(y−b), and the weighted residual statement is

∫0b∫0a[−2α[y(y−b)+x(x−a)]−c]xy(x−a)(y−b)dxdy=0.

Evaluating the integrals,

α90[a3b3(a2+b2)]−a3b3c36=0⟹α=5c2(a2+b2),

so

u^(x,y)=5c2(a2+b2)xy(x−a)(y−b).

Example. Alternatively, take a double sine series, which also satisfies the boundary conditions:

u^(x,y)=∑j=1∞∑k=1∞αjksin⁡jπxasin⁡kπyb.

The Galerkin equation for each pair (j,k) is

∫0b∫0a[αjk(j2π2a2+k2π2b2)sin⁡jπxasin⁡kπyb−c]sin⁡jπxasin⁡kπybdxdy=0,

which gives

αjk=4c(cos⁡jπ−1)(cos⁡kπ−1)a2b2π4jk(b2j2+a2k2)

and

u^(x,y)=∑j=1∞∑k=1∞αjksin⁡jπxasin⁡kπyb.

The coefficient is zero whenever j or k is even, since then cos⁡jπ−1=0 or cos⁡kπ−1=0.


Natural Boundary Conditions

Integration identities

To handle natural boundary conditions we move derivatives from one function to another with the following identities.

1-D: integration by parts. On Ω=[a,b] with boundary Γ={a,b},

∫Ωudv=uv|Γ−∫Ωvdu,

where u(x),v(x) are integrable functions of x.

2-D: Green's theorem. For a region Ω with closed boundary Γ,

∮Γαdx+βdy=∬Ω(∂β∂x−∂α∂y)dΩ,

where α,β are suitably defined differentiable and integrable functions.

3-D: the divergence theorem.

∫ΓA→⋅n→dΓ=∫Ω∇⋅A→dΩ,

where

A→=Axi^+Ayj^+Azk^,∇=i^∂∂x+j^∂∂y+k^∂∂z,n→=nxi^+nyj^+nzk^,∇⋅A→=∂Ax∂x+∂Ay∂y+∂Az∂z.

A natural boundary condition in one dimension

Example. Consider

ϕ″−kϕ=0,0≤x≤L,k constant,

ϕ=0  at x=0,dϕdx=q¯  (given) at x=L.

Take the approximation

ϕ^=ψ+∑m=1MamNm,

where ψ satisfies the boundary condition, the am are constants, and the Nm are the shape (trial) functions, here polynomials. The trial functions must vanish on the part of the boundary where the Dirichlet boundary condition is given, i.e. Nm=0 at x=0. Let

Nm=xm,so Nm=0 at x=0.

The weighted residual statement contains a domain residual and a boundary residual:

∫ΩℰΩwℓdΩ+∫ΓℰΓw¯ℓdΓ=0.

Galerkin approach: wℓ=Nℓ (weight function = shape function) and w¯ℓ=−wℓ. Since Nℓ=0 at x=0, the boundary term is needed only at x=L:

∫0L(d2ϕ^dx2−kϕ^)Nℓdx−Nℓ(dϕ^dx−q¯)|x=L=0.

Applying integration by parts to the first term,

−∫0Ldϕ^dxdNℓdxdx−k∫0Lϕ^Nℓdx+Nℓdϕ^dx|x=L−Nℓdϕ^dx|x=0−Nℓ(dϕ^dx−q¯)|x=L=0.

The term at x=0 vanishes and the terms with ϕ^′(L) cancel, so

∫0Ldϕ^dxdNℓdxdx+k∫0Lϕ^Nℓdx=q¯Nℓ|x=L.

The natural boundary condition ϕ′(L)=q¯ now appears only on the right-hand side. Substituting ϕ^,

∫0L(dψdx+∑m=1MamdNmdx)dNℓdxdx+k∫0L(ψ+∑m=1MamNm)Nℓdx=q¯Nℓ|x=L,ℓ=1,…,M.

This is a linear system

[K]M×M{a}M×1={P}M×1,{a}=[a1a2⋯aM]T,

where {P} is a known vector and

Kℓm=∫0LdNℓdxdNmdxdx+k∫0LNℓNmdx,Pℓ=q¯Nℓ|x=L−∫0LdψdxdNℓdxdx−k∫0LψNℓdx.

Remark. We will use the Galerkin approach throughout the course.


The General Weighted Residual Statement

Suppose

Lϕ+f=0  in Ω,Mϕ+r=0  on the boundary ∂Ω=Γ,

where L is a differential operator and M is a boundary operator. For example,

L=∇2=∂2∂x2+∂2∂y2,L=∇4=∇2(∇2)=∂4∂x4+2∂4∂x2∂y2+∂4∂y4.

Example. The Laplace equation

∇2ϕ=0  in Ω,ϕ=ϕ¯  on Γ1,q=∂ϕ∂n=q¯  on Γ2

has this form with

L=∇2,f=0;M=1, r=−ϕ¯  on Γ1;M=∂∂n, r=−q¯  on Γ2.

Let ϕ^=ψ+∑m=1MamNm. The residuals are

ℰΩ=Lϕ^+f,ℰΓ=Mϕ^+r.

The weighted residual statement in general takes one of two forms:

(1)∫ΩℰΩwdΩ=∫Ω(Lϕ^+f)wdΩ=0,(2)∫ΩℰΩwdΩ+∫ΓℰΓw¯dΓ=0,

where w and w¯ are weight functions.

Form (1) with Galerkin weights wℓ=Nℓ gives

∑m=1Mam∫Ω(LNm)NℓdΩ+∫Ω(Lψ)NℓdΩ+∫ΩfNℓdΩ=0,ℓ=1,…,M,

that is, [K]{a}={P} with

Kℓm=∫ΩNℓ(LNm)dΩ,Pℓ=−∫ΩNℓfdΩ−∫ΩNℓ(Lψ)dΩ.

Form (2) with wℓ=Nℓ and w¯ℓ=−Nℓ (taking ψ=0) gives

∫Ω(Lϕ^+f)NℓdΩ−∫Γ(Mϕ^+r)NℓdΓ=0,

∑m=1Mam∫Ω(LNm)NℓdΩ+∫ΩfNℓdΩ−∑m=1Mam∫Γ(MNm)NℓdΓ−∫ΓrNℓdΓ=0.

The terms multiplying am form the matrix and the terms with f and r form the vector: [K′]{a}={P′} with

Kℓm′=∫Ω(LNm)NℓdΩ−∫Γ(MNm)NℓdΓ,Pℓ′=−∫ΩfNℓdΩ+∫ΓrNℓdΓ.


The BEM for the Laplace and Poisson Equations

Consider

{∇2ϕ=fin a region Ω⊂ℝ2, f=f(x,y) a known function,ϕ=ϕ¯given on Γ1(forced, Dirichlet),q=∂ϕ∂n=q¯given on Γ2(natural, Neumann).

Definition. A boundary condition prescribing the value of the unknown function, ϕ=ϕ¯, is a forced (essential, Dirichlet) boundary condition. A boundary condition prescribing the normal derivative, q=∂ϕ/∂n=q¯, is a natural (Neumann) boundary condition.

Let ϕ be the exact solution (assume it is not available) and ϕ^ an approximate solution, with q^=∂ϕ^/∂n. Then the residuals are

ℰΩ=∇2ϕ^−f,ℰΓ1=ϕ^−ϕ¯,ℰΓ2=q^−q¯.

We use the Galerkin method to make the residuals as small as possible. The weighted residual statement is

∫ΩℰΩwℓdΩ+∫Γ1ℰΓ1w¯ℓdΓ+∫Γ2ℰΓ2w¯¯ℓdΓ=0,

with the boundary weights

w¯ℓ=∂wℓ∂n  on Γ1,w¯¯ℓ=−wℓ  on Γ2.

Substituting the residuals:

∫Ω(∇2ϕ^−f)wℓdΩ+∫Γ1(ϕ^−ϕ¯)∂wℓ∂ndΓ−∫Γ2(q^−q¯)wℓdΓ=0.

Theorem (Green's identity). ∫Ωu∇2vdΩ=∫Γu∂v∂ndΓ−∫Ω∇u⋅∇vdΩ. In two dimensions this follows from Green's theorem.

With u=wℓ, v=ϕ^:

∫Γ1+Γ2∂ϕ^∂nwℓdΓ−∫Ω∇ϕ^⋅∇wℓdΩ−∫ΩfwℓdΩ+∫Γ1(ϕ^−ϕ¯)∂wℓ∂ndΓ−∫Γ2(q^−q¯)wℓdΓ=0.

Apply Green's identity again, now with u=ϕ^, v=wℓ:

∫Γ1+Γ2q^wℓdΓ−∫Γ1+Γ2ϕ^∂wℓ∂ndΓ+∫Ωϕ^∇2wℓdΩ−∫ΩfwℓdΩ+∫Γ1(ϕ^−ϕ¯)∂wℓ∂ndΓ−∫Γ2(q^−q¯)wℓdΓ=0.

On Γ1 the terms with ϕ^ cancel, and on Γ2 the terms with q^ cancel. Therefore

(∗)∫Ωϕ^∇2wℓdΩ−∫ΩfwℓdΩ+∫Γ1(q^wℓ−ϕ¯∂wℓ∂n)dΓ+∫Γ2(q¯wℓ−ϕ^∂wℓ∂n)dΓ=0.

The last two integrals are on the boundary; the first two are on the domain, and we need to convert them or get rid of them.

  • Take f=0 to get rid of ∫ΩfwℓdΩ. (Later we will use the DRBEM to handle this inhomogeneous term with a domain integral.)
  • We still have the domain integral ∫Ωϕ^∇2wℓdΩ. To remove it, we choose wℓ so that ∇2wℓ acts like the Dirac delta function.

The Dirac delta function

Definition. For a point x′, the Dirac delta function δ(x,x′) (in some books written δ(x−x′)) satisfies δ(x,x′)=0 for x≠x′ and δ(x,x′)=∞ for x=x′, in such a way that ∭Vδ(x,x′)ϕ(x)dV={ϕ(x′),x′∈V,0,x′∉V, where x=(x,y,z) and x′=(x′,y′,z′).

Thus in the weighted residual statement the multiplier ∇2wℓ in the domain integral ∫Ωϕ^∇2wℓdΩ should be like the Dirac delta function:

∇2wℓ=δ(|r→−r→ℓ|),

where r→ℓ=(xℓ,yℓ) is the source point (like x′ in δ(x,x′)) and r→=(x,y) is a general point, so r→−r→ℓ is the position vector from the source point.

Back to the weighted residual statement (∗) with f=0:

cℓϕ^(r→ℓ)+∫Γ2(q¯wℓ−ϕ^∂wℓ∂n)dΓ+∫Γ1(q^wℓ−ϕ¯∂wℓ∂n)dΓ=0,

where cℓ is a constant: cℓ=1 for a source point inside Ω, and cℓ=12 for a source point on a smooth part of the boundary (in one dimension, at an endpoint).


Fundamental Solutions

Definition. A weight function wℓ satisfying ∇2wℓ=δ(|r→−r→ℓ|) is a fundamental solution of the Laplace equation with source point r→ℓ.

For the Laplace equation the fundamental solutions are:

1-D:wℓ=|x−xℓ|2=12[(x−xℓ)(H(x−xℓ)−H(xℓ−x))],2-D:wℓ=12πln⁡|r→−r→ℓ|,3-D:wℓ=−14π|r→−r→ℓ|,

where H is the Heaviside function.

Definition. The Heaviside function is H(x)={1,x≥0,0,x<0.

Deriving the fundamental solutions

Away from the source point, wℓ satisfies the Laplace equation, and by symmetry it depends only on the distance r=|r→−r→ℓ|.

2-D. The Laplacian in cylindrical coordinates is

∇2u=1r∂∂r(r∂u∂r)+1r2∂2u∂θ2+∂2u∂z2=0.

Assume the axisymmetric case, so only r varies:

ddr(rdudr)=0 ⇒ rdudr=A ⇒ dudr=Ar ⇒ u=Aln⁡r+B.

3-D. Use spherical coordinates, with ϕ the angle from the z-axis and θ the angle in the xy-plane:

∇2u=1r2[∂∂r(r2∂u∂r)+1sin2⁡ϕ∂2u∂θ2+1sin⁡ϕ∂∂ϕ(sin⁡ϕ∂u∂ϕ)]=0.

Assuming axisymmetry we have only the variable r:

ddr(r2dudr)=0 ⇒ r2dudr=A ⇒ dudr=Ar2 ⇒ u=C1r+B.

Computing the constants. Take a simple domain Dε around the point x′=r→ℓ=(xℓ,yℓ): an interval of length 2ε in 1-D, a disc of radius ε in 2-D, a ball of radius ε in 3-D. Integrating ∇2wℓ=δ over Dε,

∫Dε∇2wℓdV=∫Dεδ(x,x′)dV=1.

Apply the divergence theorem:

∫∂Dε∂wℓ∂ndΓ=1.

On ∂Dε the normal derivative is ∂wℓ/∂r evaluated at r=ε, which is constant, so the integral is this value times the size of ∂Dε.

  • In 3-D: dwℓdr|r=ε=−Cε2 and the sphere has area 4πε2, so −Cε2⋅4πε2=1, i.e. C=−14π.
  • In 2-D: dwℓdr|r=ε=Aε and the circle has length 2πε, so Aε⋅2πε=1, i.e. A=12π.
  • In 1-D: wℓ=A|x−xℓ|, and the two endpoints contribute A+A=1, so A=12.

In both 2-D and 3-D the fundamental solutions are, up to a constant,

wℓ=12πln⁡r+B,wℓ=−14πr+B,

and B=0 is taken from the boundary condition on wℓ.


A One-Dimensional Example

Example. Consider the (well-defined) problem

d2ϕdx2=0,0<x<1,ϕ(0)=1,dϕdx|x=1=−3.

Find ϕ(1) and ϕ(x) for 0<x<1.

In one dimension the boundary consists of the endpoints x=0 and x=1, and the outward normal derivative is d/dx at x=1 and −d/dx at x=0. Thus q¯=−3 is given at x=1, ϕ¯=1 is given at x=0, and the unknowns are ϕ^(1) and q^0, the normal derivative at x=0. The weighted residual statement becomes

cℓϕ^(xℓ)+(q¯wℓ−ϕ^dwℓdx)|x=1+(q^0wℓ−ϕ¯(−dwℓdx))|x=0=0,

with cℓ=12 at xℓ=0 and xℓ=1, and cℓ=1 inside. Substitute wℓ=|x−xℓ|2:

dwℓdx=ddx[|x−xℓ|2]=12ddx[(x−xℓ)[H(x−xℓ)−H(xℓ−x)]]=12[H(x−xℓ)−H(xℓ−x)].

Then

cℓϕ(xℓ)+[(−3)|x−xℓ|2−ϕ^H(x−xℓ)−H(xℓ−x)2]x=1+[q^0|x−xℓ|2+(1)H(x−xℓ)−H(xℓ−x)2]x=0=0,

that is,

cℓϕ(xℓ)−3(1−xℓ)2−ϕ^(1)2[H(1−xℓ)−H(xℓ−1)]+q^02xℓ+H(0−xℓ)−H(xℓ−0)2=0.

Since

H(1−xℓ)−H(xℓ−1)={1,xℓ<1,0,xℓ=1,H(0−xℓ)−H(xℓ−0)={−1,xℓ>0,0,xℓ=0,

we get, with the Kronecker delta,

cℓϕ(xℓ)−32(1−xℓ)−ϕ^(1)2(1−δxℓ1)+q^02xℓ+δ0xℓ−12=0.

At xℓ=0 (cℓ=12, ϕ(0)=1): 12ϕ(0)−32−ϕ^(1)2=0 ⇒ ϕ^(1)=−2.

At xℓ=1 (cℓ=12): 12ϕ(1)+q^02−12=0 ⇒ q^0=3.

Inside (cℓ=1): ϕ(xℓ)−32(1−xℓ)+1+32xℓ−12=0 ⇒ ϕ(xℓ)+3xℓ−1=0 ⇒ ϕ(xℓ)=1−3xℓ.

This is the exact solution: ϕ″=0, ϕ(0)=1, ϕ′(1)=−3. The value q^0=3 is the outward normal derivative −ϕ′(0) at x=0.


Flashcards

One card for every definition in these notes. Tap or click a card (or press the space bar) to see the definition; use the arrows to move between cards.

Boundary element method (BEM)

A method for solving differential equations, especially PDEs, that discretizes only the boundary of the region instead of the whole domain, which gives a smaller linear system.

Residual ℰ(x)

The function obtained by substituting an approximate solution y^ into the left-hand side of the equation written as (…)=0, e.g. ℰ(x)=y^′−x+y^ for y′=x−y. It is zero exactly when y^ solves the equation.

Collocation method

Choose as many points xi as there are unknown coefficients and require the residual to vanish there: ℰ(xi)=0.

Method of moments

Use the monomials 1,x,x2,… as weighting functions: ∫01ℰ(x)xidx=0, one equation per unknown coefficient.

Galerkin method

Use the same functions that appear in the approximation (the shape functions) as weighting functions: ∫ℰNℓ=0 for each shape function Nℓ.

Subdomain method

Divide the region into as many subintervals as there are unknown coefficients and set the integral of the residual over each subinterval to zero.

Method of least squares

Choose the coefficients to minimize ∫ℰ2dx, i.e. set ∂∂a∫ℰ2dx=0 for each coefficient a.

Forced (Dirichlet) and natural (Neumann) boundary conditions

A forced (essential, Dirichlet) condition prescribes the function, ϕ=ϕ¯; a natural (Neumann) condition prescribes the normal derivative, q=∂ϕ/∂n=q¯.

Dirac delta function δ(x,x′)

Zero for x≠x′ and infinite at x=x′, so that ∭Vδ(x,x′)ϕ(x)dV=ϕ(x′) if x′∈V and 0 if x′∉V.

Fundamental solution of the Laplace equation

A weight function wℓ with ∇2wℓ=δ(|r→−r→ℓ|): |x−xℓ|2 in 1-D, 12πln⁡|r→−r→ℓ| in 2-D, −14π|r→−r→ℓ| in 3-D.

Heaviside function H(x)

H(x)=1 for x≥0 and H(x)=0 for x<0.