This page was generated from unit-11.2-bem-Laplace/Laplace_DtN_indirect.ipynb.
11.2.1 Dirichlet Laplace Indirect MethodΒΆ
keys: homogeneous Dirichlet bvp, single layer potential, ACA
[1]:
from netgen.occ import *
from ngsolve import *
from ngsolve.webgui import Draw
from ngsolve.bem import *
from ngsolve import Projector, Preconditioner
from ngsolve.krylovspace import CG
We consider the Dirichlet boundary value problem
Let us choose the following ansatz for the solution \(u\in H^1(\Omega)\) (indirect ansatz)
and solve for the density \(j\in H^{-\frac12}(\Gamma)\) by the boundary element method, i.e. the numerical solution of the variational formulation
Define the geometry \(\Omega \subset \mathbb R^3\) and create a mesh:
[2]:
sp = Sphere( (0,0,0), 1)
mesh = Mesh( OCCGeometry(sp).GenerateMesh(maxh=0.3)).Curve(4)
Create test and trial function finite element spaces for \(H^{-\frac12}(\Gamma)\) according to the given mesh:
[3]:
fesL2 = SurfaceL2(mesh, order=4, dual_mapping=True)
u,v = fesL2.TnT()
Define Dirichlet data \(u_0\) and compute the right hand side vector:
[4]:
u0 = 1/ sqrt( (x-1)**2 + (y-1)**2 + (z-1)**2 )
rhs = LinearForm (u0*v.Trace()*ds(bonus_intorder=3)).Assemble()
The discretisation of the above variational formulation leads to a system of linear equations, ie
where \(\mathrm{V}\) is the single layer potential operator. \(\mathrm V\) is regular and symmetric.
Demo 1: Assemble the single layer operator \(V\) as dense matrix and solve for unknwon density \(j\):
[5]:
j = GridFunction(fesL2)
pre = BilinearForm(u*v*ds, diagonal=True).Assemble().mat.Inverse()
with TaskManager():
    # V = SingleLayerPotentialOperator(fesL2, intorder=10)
    V = LaplaceSL(u*ds)*v*ds
    CG(mat = V.mat, pre=pre, rhs = rhs.vec, sol=j.vec, tol=1e-8, maxsteps=200, initialize=False, printrates=True)
CG iteration 1, residual = 2.185606954606291
CG iteration 2, residual = 0.5510990298605161
CG iteration 3, residual = 0.13112741187311439
CG iteration 4, residual = 0.031078921260809377
CG iteration 5, residual = 0.0073724819751242775
CG iteration 6, residual = 0.00179293713597407
CG iteration 7, residual = 0.0005259800974227637
CG iteration 8, residual = 0.00025807777880645634
CG iteration 9, residual = 0.0002832379462793779
CG iteration 10, residual = 0.00012036639800572769
CG iteration 11, residual = 0.0002350724690603451
CG iteration 12, residual = 4.492369420146522e-05
CG iteration 13, residual = 4.801474721087475e-05
CG iteration 14, residual = 2.782522946273216e-05
CG iteration 15, residual = 1.4627805195033825e-05
CG iteration 16, residual = 1.478100510708242e-05
CG iteration 17, residual = 9.771555001043707e-06
CG iteration 18, residual = 6.175954890987752e-06
CG iteration 19, residual = 1.53874036890296e-05
CG iteration 20, residual = 3.2801503124344916e-06
CG iteration 21, residual = 2.9700673587944344e-06
CG iteration 22, residual = 3.493169500408977e-06
CG iteration 23, residual = 2.5937980739564723e-06
CG iteration 24, residual = 1.2604332720605243e-06
CG iteration 25, residual = 8.372891101723178e-07
CG iteration 26, residual = 1.244041116161955e-06
CG iteration 27, residual = 5.872586650221068e-07
CG iteration 28, residual = 4.414618651914607e-07
CG iteration 29, residual = 5.781842124955137e-07
CG iteration 30, residual = 3.526775260340653e-07
CG iteration 31, residual = 1.7782255323551035e-07
CG iteration 32, residual = 1.544194162836577e-07
CG iteration 33, residual = 2.1911331582042747e-07
CG iteration 34, residual = 1.2550259371570132e-07
CG iteration 35, residual = 8.723802300989834e-08
CG iteration 36, residual = 6.888878393108547e-08
CG iteration 37, residual = 1.0272145825994939e-07
CG iteration 38, residual = 3.437270116727615e-08
CG iteration 39, residual = 4.023999056169018e-08
CG iteration 40, residual = 5.732896601144878e-08
CG iteration 41, residual = 1.8547250501583977e-08
[6]:
Draw (j, order=3);
Notes:
- For details on the analysis of boundary integral equations derived from elliptic partial differential equations, see for instance Strongly Elliptic Systems and Boundary Integral Equations. 
- The integration of singular pairings is done as proposed in Randelementmethoden 
- The adaptive cross approximation is done as proposed in Hierarchical Matrices. 
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