Sampling and low-rank tensor approximations

transcript

Sampling and Low-Rank Tensor Approximations

Hermann G. Matthies∗

Alexander Litvinenko∗, Tarek A. El-Moshely+

∗TU Braunschweig, Brunswick, Germany+MIT, Cambridge, MA, USA

wire@tu-bs.de

http://www.wire.tu-bs.de

$Id: 12_Sydney-MCQMC.tex,v 1.3 2012/02/12 16:52:28 hgm Exp $

Overview

1. Functionals of SPDE solutions

2. Computing the simulation

3. Parametric problems

4. Tensor products and other factorisations

5. Functional approximation

6. Emulation approximation

7. Examples and conclusion

TU Braunschweig Institute of Scientific Computing

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Problem statement

We want to compute

Jk = E (Ψk(·, ue(·))) =

Ψk(ω, ue(ω))P(dω),

where P is a probability measure on Ω, and

ue is the solution of a PDE depending on the parameter ω ∈ Ω.

A[ω](ue(ω)) = f(ω) a.s. in ω ∈ Ω,ue(ω) is a U-valued random variable (RV).

To compute an approximation uM(ω) to ue(ω) via

simulation is expensive, even for one value of ω, let alone for

Jk ≈N∑n=1

Ψk(ωn,uM(ωn)) wn

Not all Ψk of interest are known from the outset.

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Example: stochastic diffusion

Aquifer

Geometry

2D Model

Simple stationary model of groundwater flow with stochastic data κ, f

−∇ · (κ(x, ω)∇u(x, ω)) = f(x, ω) x ∈ D ⊂ Rd & b.c.

Solution is in tensor space S ⊗ U =:W, e.g. W = L2(Ω,P)⊗ H1(D)

leads after Galerkin discretisation with UM = spanvmMm=1 ⊂ U to

A[ω](uM(ω)) = f(ω) a.s. in ω ∈ Ω,where uM(ω) =

∑Mm=1 um(ω)vm ∈ S ⊗ UM .

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Realisation of κ(x, ω)

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Solution example

Geometry

flow out

Dirichlet b.c.

flow = 0 Sources

Realization of κ

Realization of solution

Mean of solution

Variance of solution

−1−0.5

−0.5

Pru(x) > 8

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Computing the simulation

To simulate uM one needs samples of the random field (RF) κ,

which depends on infinitely many random variables (RVs).

This has to be reduced / transformed Ξ : Ω → [0, 1]s to a finite number

s of RVs ξ = (ξ1, . . . , ξs), with µ = Ξ∗P the push-forward measure:

Ψk(ω, ue(ω))P(dω) ≈∫[0,1]s

Ψk(ξ,uM(ξ))µ(dξ).

This is a product measure for independent RVs (ξ1, . . . , ξs).

Approximate expensive simulation uM(ξ) by cheaper emulation.

Both tasks are related by viewing uM : ξ 7→ uM(ξ), or κ1 : x 7→ κ(x, ·)(RF indexed by x), or κ2 : ω 7→ κ(·, ω) (function valued RV),

maps from a set of parameters into a vector space.

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Parametric problems and RKHS

For each p in a parameter set P, let r(p) be an

‘object’ in a Hilbert space V (for simplicity).

With r : P → V, denote U = span r(P) = span im r, then

to each function r : P → U corresponds a linear map R : U → R:

R : U 3 v 7→ 〈r(·)|v〉V ∈ R = imR ⊂ RP.(sometimes called a weak distribution)

By construction R is injective. Use this to make R a pre-Hilbert space:

∀φ, ψ ∈ R : 〈φ|ψ〉R := 〈R−1φ|R−1ψ〉U .R−1 is unitary on completion R which is a RKHS — reproducing kernel

Hilbert space with kernel ρ(p1, p2) = 〈r(p1)|r(p2)〉U .

Functions in R are in one-to-one correspondence with elements of U .

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‘Covariance’

If Q ⊂ RP is Hilbert with inner product 〈·|·〉Q; e.g. Q = L2(P, ν),

define in U a positive self-adjoint map—the covariance C = R∗R

〈Cu|v〉U = 〈Ru|Rv〉Q, ⇒ has spectrum σ(C) ⊆ R+,

with spectral projectors Eλ : C =∫∞0λ dEλ

Similarly, define C : Q → Q for φ, ψ ∈ Q such that C = RR∗ by

〈Cφ|ψ〉Q = 〈R∗φ|R∗ψ〉U ⇒ has same spectrum as C : σ(C) = σ(C),

and unitarily equivalent projectors Eλ = WEλW∗ : C =

∫∞0λ dEλ.

Spectrum and projectors (σ(C), Eλ) are essence of r(p).

Specifically, for φ, ψ ∈ L2(P, ν) we have

〈R∗φ|R∗ψ〉U =

∫∫P×P

φ(p1)ρ(p1, p2)ψ(p2) ν(dp1) ν(dp2).

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‘Covariance’ operator and SVD

Spectral decomposition with projectors Eλ

∫ ∞0

λ dEλv =∑

λj∈σp(C)

λj〈ej|v〉U ej +

∫R+\σp(C)

λ dEλv.

C unitarily equivalent to multiplication operator Mk with non-negative k:

C = U∗MkU = (U∗M1/2k )(M

1/2k U), with M

1/2k = M√k.

This connects to the singular value decomposition (SVD)

of R = VM1/2k U , with a (partial) isometry V .

Often C has a pure point spectrum (e.g. C compact)

⇒ last integral vanishes.

In general—to show tensors—we have to invoke generalised eigenvectors

and Gelfand triplets (rigged Hilbert spaces) for the continuous spectrum.

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SVD, Karhunen-Loeve-expansion, and tensors

For sake of simplicity assume σ(C) = σp(C).

C =∑j

λj〈ej|·〉Uej =∑j

λj ej ⊗ ej.

(Rv)(p) = 〈r(p)|v〉U =∑j

√λj 〈ej|v〉U sj(p)

with sj := Rej with R =∑j

√λj (sj ⊗ ej), or

R∗ =∑j

√λj (ej ⊗ sj), r(p) =

√λj sj(p)ej, r ∈ S ⊗ U .

The singular value decomposition, a.k.a. Karhunen-Loeve-expansion.

A sum of rank-1 operators / tensors.

In general C =∫R+λ〈eλ, ·〉eλ %(dλ) with generalised eigenvectors eλ.

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Examples and interpretations

• If V is a space of centred random variables (RVs), r is a random field

or stochastic process indexed by P, then C represented by the kernel

ρ(p1, p2) is the covariance function.

• If in this case P = Rd and moreover ρ(p1, p2) = c(p1 − p2) (stationary

process / homogeneous field), then the diagonalisation U is effected

by the Fourier transform, and the point spectrum is typically empty.

• If ν is a probability measure (ν(P) = 1), and r is a V-valued RV, then

C is the covariance operator.

• If P = 1, 2, . . . , n and R = Rn, then ρ is the Gram matrix of the

vectors r1, . . . , rn. If n < dimV, the map R can be seen as a model

reduction projector.

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Factorisations / re-parametrisations

R∗ serves as representation for Karhunen-Loeve expansion.

This is a factorisation of C. Some other possible ones:

C = R∗R = (VM1/2k )(VM

1/2k )∗ = C1/2C1/2 = B∗B,

where C = B∗B is an arbitrary one.

Each factorisation leads to a representation—all unitarily equivalent.

(When C is a matrix, a favourite is Cholesky: C = LL∗).

Assume that C = B∗B and B : U → H −→ r ∈ U ⊗H.

Select a orthonormal basis ek in H.

Unitary Q : `2 3 a = (a1, a2, . . .) 7→∑k akek ∈ H.

Approximation possible by injection P ∗s : Rs→ `2.

Let r(a) := B∗Qa := R∗a (linear in a), i.e. R∗ : `2→ U . Then

R∗R = (B∗Q)(Q∗B) = B∗B = C.

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Representations

Several representions for ‘object’ r(p) ∈ U in a simpler space.

• The RKHS

• The Karhunen-Loeve expansion based on spectral decomposition of C.

• The multiplicative spectral decomposition, as VM1/2k maps into U .

• Arbitrary factorisations C = B∗B.

• Analogous: consider C instead of C. If Q = L2(P, ν) this leads to

integral transforms, the kernel decompositions.

These can all be used for model reduction, choosing a smaller subspace.

Applied to RF κ(x, ω), and hence to uM(ω), yielding uM(ξ).

Can again be applied to uM(ξ).

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Functional approximation

Emulation — replace expensive simulation uM(ξ) by inexpensive

approximation / emulation uE(ξ) ≈ uM(ξ)

( alias response surfaces, proxy / surrogate models, etc.)

Choose subspace SB ⊂ S with basis XβBβ=1,

make ansatz for each um(ξ) ≈∑β u

βmXβ(ξ), giving

uE(ξ) =∑m,β

uβmXβ(ξ)vm =∑m,β

uβmXβ(ξ)⊗ vm.

Set U = (uβm) — (M ×B).

Sampling, we generate matrix / tensor

U = [uM(ξ1), . . . ,uM(ξN)] = (um(ξn))nm — (M ×N).

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Tensor product structure

Story does not end here as one may choose S =⊗

approximated by SB =⊗K

k=1 SBk, with SBk ⊂ Sk.

Solution represented as a tensor of grade K + 1

in WB,N =(⊗K

k=1 SBk)⊗ UN .

For higher grade tensor product structure, more reduction is possible,

— but that is a story for another talk, here we stay with K = 1.

With orthonormal Xβ one has

uβm =

∫[0,1]s

Xβ(ξ)um(ξ)µ(dξ) ≈N∑n=1

wnXβ(ξn)um(ξn).

Let W = diag (wn)—(N ×N), X = (Xβ(ξn)) — (B ×N), hence

U = U(WXT ). For B = N this is just a basis change.

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Low-rank approximation

Focus on array of numbers U := [um(ξn)], view as matrix / tensor:N∑n=1

M∑m=1

Um,nemM ⊗ enN , with unit vectors enN ∈ RN , emM ∈ RM .

The sum has M ∗N terms, the number of entries in U .

Rank-R representation is approximation with R terms

U =N∑n=1

M∑m=1

Um,nemM(enN)T ≈

R∑`=1

a`bT` = ABT ,

with A = [a1, . . . ,aR] — (M ×R) and B = [b1, . . . , bR] — (N ×R).

It contains only R ∗ (M +N)M ∗N numbers.

We will use updated, truncated SVD. This gives for coefficients

U = U(WXT ) ≈ ABT (WXT ) = A(XWB)T =: ABT

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Emulation instead of simulation

Let x(ξ) := [X1(ξ), . . . , XB(ξ)]T . Emulator and low-rank emulator is

uE(ξ) = Ux(ξ), and uL(ξ) := ABTx(ξ).

Computing A,B: start with z samples Uz1 = [uM(ξ1), . . . ,uM(ξz)].

Compute truncated, error controled SVD:M×zUz1 ≈

R×RΣ

(z×RV

then set A1 = WΣ1/2,B1 = V Σ1/2⇒ B1.

For each n = z + 1, . . . , 2z, emulate uL(ξn) and evaluate residuum

rn := r(ξn) := f(ξn)−A[ξn](uL(ξn)). If ‖rn‖ is small, accept

unA = uL(ξn), otherwise solve for uM(ξn) and set unA = uM(ξn).

Set Uz2 = [uz+1A , . . . ,u2z

A ], compute updated SVD of [Uz1,Uz2],

⇒ A2,B2. Repeat for each batch of z samples.

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Emulator in integration

To evaluate

Ψk(ω, ue(ω))P(dω) ≈∫[0,1]s

Ψk(ξ,uM(ξ))µ(dξ),

we compute

Jk ≈N∑n=1

wnΨk(ξn,uL(ξn)).

If we are lucky, we need much fewer than N samples to find the

low-rank representation A, B for uL.

This is cheap to compute from samples, and uses only little storage.

In the integral the integrand is cheap to evaluate, and the low-rank

representation can be re-used if a new (Jk, Ψk) has to be evaluated.

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Use in MC sampling solution—sample

Example: Compressible RANS-flow around RAE air-foil.

Sample solution

turbulent kinetic energy pressure

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Use in MC sampling solution—storage

Inflow and air-foil shape uncertain.

Data compression achieved by updated SVD:

Made from 600 MC Simulations, SVD is updated every 10 samples.

M = 260, 000 N = 600

Updated SVD: Relative errors, memory requirements:rank R pressure turb. kin. energy memory [MB]

10 1.9e-2 4.0e-3 21

20 1.4e-2 5.9e-3 42

50 5.3e-3 1.5e-4 104

Dense matrix ∈ R260000×600 costs 1250 MB storage.

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Use in QMC sampling—mean

Trans-sonic flow with shock with N = 2600 samples.

Relative error for the density mean for rank R = 5, 10, 30, 50.

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Use in QMC sampling—variance

Trans-sonic flow with shock with N = 2600 samples.

Relative error for the density variance for rank R = 5, 10, 30, 50.

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Conclusion

• Random field discretisation and sampling can be seen as weak

distribution with associated covariance.

• Analysis of associated linear map reveals essential structure.

• Factorisations of covariance lead to SVD (Karhunen-Loeve

expansion) and tensor products.

• Functional approximation to construct emulator.

• Sparse and inexpensive emulation.

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Sampling and low-rank tensor approximations

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