Wavelet based matrix compression for boundary integral equations on complex geometries
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- Ωκεανός Μπουκουβαλαίοι
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1 1 Wavelet based matrix compression for boundary integral equations on complex geometries Ulf Kähler Chemnitz University of Technology Workshop on Fast Boundary Element Methods in Industrial Applications (Hirschegg)
2 2 Overview Motivation - presentation of problem
3 2 Overview Motivation - presentation of problem Wavelet basis - stiffness matrix
4 2 Overview Motivation - presentation of problem Wavelet basis - stiffness matrix Wavelet construction
5 2 Overview Motivation - presentation of problem Wavelet basis - stiffness matrix Wavelet construction Computation of the stiffness matrix
6 2 Overview Motivation - presentation of problem Wavelet basis - stiffness matrix Wavelet construction Computation of the stiffness matrix Numerical results
7 3 Preliminaries Aρ = f on Γ = Ω R 2 A : H q (Γ) H q (Γ) (Aρ)(x) = k(x, y)ρ(y) Γ y Γ
8 4 single layer potential: q = 1 2, A = K K = 1 log y x ρ(y) Γ y 2π double layer potential: q = 0, A = K K = 1 2π Γ Γ n(y), y x y x 2 ρ(y) Γ y
9 5 Galerkin scheme Variational formulation: find ρ H q (Γ) : (Aρ, v) L 2 (Γ) = (f, v) L 2 (Γ) v H q (Γ) V N = span{φ 1,..., φ N } H q (Γ) A Φ ρ Φ = f Φ
10 6 Geometry Γ N - polygonial approximations of the surface Γ finest level is fixed diam(ω) < 1
11 7 ansatzfunctions: φ i (x) = 1 Γ i Γ, for x Γ i, 0, else
12 7 ansatzfunctions: φ i (x) = 1 Γ i Γ, for x Γ i, 0, else φ i (x(s)) = 13 s Γ i 1 Γ s 13 Γ Γ+ 1 i 1 3 Γ i Γ for x(s) Γ i 1, Γ i Γ for x(s) Γ i, 0 else
13 8 Motivation - presentation of problem classical single scale method
14 8 Motivation - presentation of problem classical single scale method advantages: reduction of the space dimension good convergence rates
15 8 Motivation - presentation of problem classical single scale method advantages: reduction of the space dimension good convergence rates disadvantages: densly populated stiffness matrix expensive quadrature rules
16 8 Motivation - presentation of problem classical single scale method advantages: reduction of the space dimension good convergence rates disadvantages: densly populated stiffness matrix expensive quadrature rules
17 Objectives 9
18 9 Objectives sparse stiffness matrix
19 9 Objectives sparse stiffness matrix low complexity of solving
20 Wavelet basis - stiffness matrix 10
21 10 Wavelet basis - stiffness matrix hierarchical wavelets:
22 10 Wavelet basis - stiffness matrix hierarchical wavelets: hierarchical structure
23 10 Wavelet basis - stiffness matrix hierarchical wavelets: hierarchical structure ψ i := N k=1 ω i,kφ k, ω i1 ω i2 = δ i1 i 2
24 10 Wavelet basis - stiffness matrix hierarchical wavelets: hierarchical structure ψ i := N k=1 ω i,kφ k, ω i1 ω i2 = δ i1 i 2 (ψ i, x α ) = 0, α < d ( x α = x α 1 1 xα 2 2 )
25 10 Wavelet basis - stiffness matrix hierarchical wavelets: hierarchical structure ψ i := N k=1 ω i,kφ k, ω i1 ω i2 = δ i1 i 2 (ψ i, x α ) = 0, α < d ( x α = x α 1 1 xα 2 2 ) Dahmen-Prößdorf-Schneider/ von Petersdorff-Schwab: A ψ is a quasi sparse matrix with O(N log(n)) entries.
26 11 idea: = Γ k(x, y)ψ k (x)ψ k (y) Γ x Γ y Γ (D α+β k)(x 0, y 0 ) (α,β) N 2 0 N 2 0 Γ ψ k(x)(x x 0 ) α Γ x α! Γ ψ k (y)(y y 0) β Γ y β!
27 11 idea: = Γ k(x, y)ψ k (x)ψ k (y) Γ x Γ y Γ (D α+β k)(x 0, y 0 ) (α,β) N 2 0 N 2 0 Γ ψ k(x)(x x 0 ) α Γ x α! D α+β k(x 0, y 0 ) ( 1 C x 0 y 0 Γ ψ k (y)(y y 0) β Γ y β! ) α+β+1 2q
28 Wavelet construction 12
29 12 Wavelet construction hierarchical structure coarsening cluster tree
30 12 Wavelet construction hierarchical structure coarsening cluster tree [ ] [ ] Φ ν,j 1 V ν,j 1 Ψ ν,j 1 0 = V ν,j 1 Φ ν,j 0
31 13 Let M ν,j 1 be the moment matrix of the cluster ν from level j 1 [ ] M ν,j 1 = x α Φ ν,j dx Γ α < d
32 13 Let M ν,j 1 be the moment matrix of the cluster ν from level j 1 [ ] M ν,j 1 = x α Φ ν,j dx Γ α < d SV D M ν,j 1 = UΣV = U [S, 0] [ V ν,j 1 0 V ν,j 1 0 ]
33 13 Let M ν,j 1 be the moment matrix of the cluster ν from level j 1 [ ] M ν,j 1 = x α Φ ν,j dx Γ α < d SV D M ν,j 1 = UΣV = U [S, 0] [ V ν,j 1 0 V ν,j 1 0 ] constant/linear ansatzfunctions Ψ is orthonormal/ Riesz-basis.
34 14 complexity of computing cluster tree and wavelets: O(N)
35 15 Computation of the stiffness matrix transformation matrix Ω Ψ,Φ := (ω i,k ) N i,k=1
36 15 Computation of the stiffness matrix transformation matrix Ω Ψ,Φ := (ω i,k ) N i,k=1 A Ψ ρ Ψ = Ω Ψ,Φ A Φ Ω Ψ,Φ Ω Ψ,Φ ρ Φ = Ω Ψ,Φ (f, φ i ) N i=1 = f Ψ
37 15 Computation of the stiffness matrix transformation matrix Ω Ψ,Φ := (ω i,k ) N i,k=1 A Ψ ρ Ψ = Ω Ψ,Φ A Φ Ω Ψ,Φ Ω Ψ,Φ ρ Φ = Ω Ψ,Φ (f, φ i ) N i=1 = f Ψ calculation of f Ψ in O(N) possible
38 16 Ω Ψ,Φ = ψ ν ψ ν1 1 0 ψ ν0 1 0 ψ ν ψ ν ψ ν ψ ν
39 16 Ω Ψ,Φ = ψ ν ψ ν1 1 0 ψ ν0 1 0 ψ ν ψ ν ψ ν ψ ν ψ ν2 1 ψ ν1 1 ψ ν0 1 ψ ν2 2.. ψ ν2 3 ψ ν1 2. ψ ν2 4..
40 16 Ω Ψ,Φ = ψ ν ψ ν1 1 0 ψ ν0 1 0 ψ ν ψ ν ψ ν ψ ν O(log(N)) columns ψ ν2 1 ψ ν1 1 ψ ν0 1 ψ ν2 2.. ψ ν2 3 ψ ν1 2. ψ ν2 4..
41 17 Using multipole method the multipole method
42 17 Using multipole method the multipole method iterative solving of A Φ ρ Φ = f Φ
43 17 Using multipole method the multipole method iterative solving of A Φ ρ Φ = f Φ fast matrix-vector product
44 17 Using multipole method the multipole method iterative solving of A Φ ρ Φ = f Φ fast matrix-vector product expansion of kernel
45 17 Using multipole method the multipole method iterative solving of A Φ ρ Φ = f Φ fast matrix-vector product expansion of kernel k(x, y) = (D α+β k)(x 0, y 0 ) (α,β) N 2 0 N 2 0 (x x 0 ) α (y y 0 ) β α! β!
46 low rank approximation of parts of A Φ 18
47 18 low rank approximation of parts of A Φ (A Φ i,j) i I,j J XkY
48 18 low rank approximation of parts of A Φ subdivision of A Φ (A Φ i,j) i I,j J XkY
49 18 low rank approximation of parts of A Φ subdivision of A Φ hierarchical matrix (A Φ i,j) i I,j J XkY
50 18 low rank approximation of parts of A Φ (A Φ i,j) i I,j J XkY subdivision of A Φ hierarchical matrix cluster-cluster interactions possible
51 18 low rank approximation of parts of A Φ (A Φ i,j) i I,j J XkY subdivision of A Φ hierarchical matrix cluster-cluster interactions possible complexity of a matrix-vector product: O(N log 2 (N))
52 18 low rank approximation of parts of A Φ (A Φ i,j) i I,j J XkY subdivision of A Φ hierarchical matrix cluster-cluster interactions possible complexity of a matrix-vector product: O(N log 2 (N))
53 application on wavelets 19
54 19 application on wavelets using fast matrix-vector products on ψ ν2 1 ψ ν1 1 ψ ν0 1 ψ ν2 2.. ψ ν2 3 ψ ν1 2. ψ ν2 4..
55 19 application on wavelets using fast matrix-vector products on ψ ν2 1 ψ ν1 1 ψ ν0 1 ψ ν2 2.. ψ ν2 3 ψ ν1 2. ψ ν2 4.. complexity of computing A Ψ : O(N log 3 (N))
56 19 application on wavelets using fast matrix-vector products on ψ ν2 1 ψ ν1 1 ψ ν0 1 ψ ν2 2.. ψ ν2 3 ψ ν1 2. ψ ν2 4.. complexity of computing A Ψ : O(N log 3 (N))
57 H.Harbecht: Error estimates for entries of A Ψ 20
58 Numerical results 21
59 Future research 22
60 22 Future research application to the 3D-case
61 22 Future research application to the 3D-case improved combination of multipole and wavelets
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