Tensor completion for PDEs with uncertain coefficients and Bayesian update surrogate

Tensor completion for PDEs with uncertain
coefficients and Bayesian Update
Alexander Litvinenko
(joint work with E. Zander, B. Rosic, O. Pajonk, H. Matthies)
Center for Uncertainty
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http://sri-uq.kaust.edu.sa/
Extreme Computing Research Center, KAUST
Alexander Litvinenko (joint work with E. Zander, B. Rosic, O. Pajonk, H. Matthies)Tensor completion for PDEs with uncertain coefficients and B

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The structure of the talk
Part I (Stochastic forward problem):
1. Motivation
2. Elliptic PDE with uncertain coefﬁcients
3. Discretization and low-rank tensor approximations
Part II (Bayesian update):
1. Bayesian update surrogate
2. Examples
Part III (Tensor completion):
1. Problem setup
2. Tensor completion for Bayesian Update

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Motivation to do Uncertainty Quantification (UQ)
Motivation: there is an urgent need to quantify and reduce the
uncertainty in output quantities of computer simulations within
complex (multiscale-multiphysics) applications.
Typical challenges: classical sampling methods are often very
inefficient, whereas straightforward functional representations
are subject to the well-known Curse of Dimensionality.
Nowadays computational predictions are used in critical
engineering decisions and thanks to modern computers we are
able to simulate very complex phenomena. But, how reliable
are these predictions? Can they be trusted?
Example: Saudi Aramco currently has a simulator,
GigaPOWERS, which runs with 9 billion cells. How sensitive
are the simulation results with respect to the unknown reservoir
properties?
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Part I: Stochastic forward problem
Part I: Stochastic Galerkin method to solve
elliptic PDE with uncertain coefﬁcients

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PDE with uncertain coefficient and RHS
Consider
− div(κ(x, ω) u(x, ω)) = f(x, ω) in G × Ω, G ⊂ R2,
u = 0 on ∂G,
(1)
where κ(x, ω) - uncertain diffusion coefficient. Since κ positive,
usually κ(x, ω) = eγ(x,ω).
For well-posedness see [Sarkis 09, Gittelson 10, H.J.Starkloff
11, Ullmann 10].
Further we will assume that covκ(x, y) is given.
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My previous work
After applying the stochastic Galerkin method, obtain:
Ku = f, where all ingredients are represented in a tensor format
Compute max{u}, var(u), level sets of u, sign(u)
[1] Efficient Analysis of High Dimensional Data in Tensor Formats,
Espig, Hackbusch, A.L., Matthies and Zander, 2012.
Research which ingredients influence on the tensor rank of K
[2] Efficient low-rank approximation of the stochastic Galerkin matrix in tensor formats,
Wähnert, Espig, Hackbusch, A.L., Matthies, 2013.
Approximate κ(x, ω), stochastic Galerkin operator K in Tensor
Train (TT) format, solve for u, postprocessing
[3] Polynomial Chaos Expansion of random coefficients and the solution of stochastic
partial differential equations in the Tensor Train format, Dolgov, Litvinenko, Khoromskij, Matthies, 2016.
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Canonical and Tucker tensor formats
Deﬁnition and Examples of tensors
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Canonical and Tucker tensor formats
[Pictures are taken from B. Khoromskij and A. Auer lecture course]
Storage: O(nd ) → O(dRn) and O(Rd + dRn).
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Definition of tensor of order d
Tensor of order d is a multidimensional array over a d-tuple
index set I = I1 × · · · × Id ,
A = [ai1...id
: iµ ∈ Iµ] ∈ RI
, Iµ = {1, ..., nµ}, µ = 1, .., d.
A is an element of the linear space
Vn =
d
µ=1
Vµ, Vµ = RIµ
equipped with the Euclidean scalar product ·, · : Vn × Vn → R,
defined as
A, B :=
(i1...id )∈I
ai1...id
bi1...id
, for A, B ∈ Vn.
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Discretization of elliptic PDE
Now let us discretize our diffusion equation with
uncertain coefﬁcients
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Karhunen Loéve and Polynomial Chaos Expansions
Apply both
Karhunen Loéve Expansion (KLE):
κ(x, ω) = κ0(x) + ∞
j=1 κjgj(x)ξj(θ(ω)), where
θ = θ(ω) = (θ1(ω), θ2(ω), ..., ),
ξj(θ) = 1
κj G (κ(x, ω) − κ0(x)) gj(x)dx.
Polynomial Chaos Expansion (PCE)
κ(x, ω) = α κ(α)(x)Hα(θ), compute ξj(θ) = α∈J ξ
(α)
j Hα(θ),
where ξ
(α)
j = 1
κj G κ(α)(x)gj(x)dx.
Further compute ξ
(α)
j ≈ s
=1(ξ )j
∞
k=1(ξ , k )αk
.
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Final discretized stochastic PDE
Ku = f, where
K:= s
=1 K ⊗ M
µ=1 ∆ µ, K ∈ RN×N, ∆ µ ∈ RRµ×Rµ ,
u:= r
j=1 uj ⊗ M
µ=1 ujµ, uj ∈ RN, ujµ ∈ RRµ ,
f:= R
k=1 fk ⊗ M
µ=1 gkµ, fk ∈ RN and gkµ ∈ RRµ .
(Wahnert, Espig, Hackbusch, Litvinenko, Matthies, 2011)
Examples of stochastic Galerkin matrices:
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Part II
Part II: Bayesian update
We will speak about Gauss-Markov-Kalman ﬁlter for the
Bayesian updating of parameters in comput. model.

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Mathematical setup
Consider
K(u; q) = f ⇒ u = S(f; q),
where S is solution operator.
Operator depends on parameters q ∈ Q,
hence state u ∈ U is also function of q:
Measurement operator Y with values in Y:
y = Y(q; u) = Y(q, S(f; q)).
Examples of measurements:
y(ω) = D0
u(ω, x)dx, or u in few points
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Random QoI
With state u a RV, the quantity to be measured
y(ω) = Y(q(ω), u(ω)))
is also uncertain, a random variable.
Noisy data: ˆy + (ω),
where ˆy is the “true” value and a random error .
Forecast of the measurement: z(ω) = y(ω) + (ω).
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Conditional probability and expectation
Classically, Bayes’s theorem gives conditional probability
P(Iq|Mz) =
P(Mz|Iq)
P(Mz)
P(Iq) (or πq(q|z) =
p(z|q)
Zs
pq(q));
Expectation with this posterior measure is conditional
expectation.
Kolmogorov starts from conditional expectation E (·|Mz),
from this conditional probability via P(Iq|Mz) = E χIq
|Mz .
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Conditional expectation
The conditional expectation is deﬁned as
orthogonal projection onto the closed subspace L2(Ω, P, σ(z)):
E(q|σ(z)) := PQ∞ q = argmin˜q∈L2(Ω,P,σ(z)) q − ˜q 2
L2
The subspace Q∞ := L2(Ω, P, σ(z)) represents the available
information.
The update, also called the assimilated value
qa(ω) := PQ∞ q = E(q|σ(z)), is a Q-valued RV
and represents new state of knowledge after the measurement.
Doob-Dynkin: Q∞ = {ϕ ∈ Q : ϕ = φ ◦ z, φ measurable}.
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Numerical computation of NLBU
Look for ϕ such that q(ξ) = ϕ(z(ξ)), z(ξ) = y(ξ) + ε(ω):
ϕ ≈ ˜ϕ =
α∈Jp
ϕαΦα(z(ξ))
and minimize q(ξ) − ˜ϕ(z(ξ)) 2
L2
, where Φα are polynomials
(e.g. Hermite, Laguerre, Chebyshev or something else).
Taking derivatives with respect to ϕα:
∂
∂ϕα
q(ξ) − ˜ϕ(z(ξ)), q(ξ) − ˜ϕ(z(ξ)) = 0 ∀α ∈ Jp
Inserting representation for ˜ϕ, obtain:
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∂
∂ϕα
E

q2
(ξ) − 2
β∈J
qϕβΦβ(z) +
β,γ∈J
ϕβϕγΦβ(z)Φγ(z)


= 2E

−qΦα(z) +
β∈J
ϕβΦβ(z)Φα(z)


= 2


β∈J
E [Φβ(z)Φα(z)] ϕβ − E [qΦα(z)]

 = 0 ∀α ∈ J .
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Now, rewriting the last sum in a matrix form, obtain the linear
system of equations (=: A) to compute coefﬁcients ϕβ:



... ... ...
... E [Φα(z(ξ))Φβ(z(ξ))]
...
... ... ...







...
ϕβ
...



 =




...
E [q(ξ)Φα(z(ξ))]
...



 ,
where α, β ∈ J , A is of size |J | × |J |.
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We can rewrite the system above in the compact form:
[Φ] [diag(...wi...)] [Φ]T




...
ϕβ
...



 = [Φ]


w0q(ξ0)
...
wNq(ξN)


[Φ] ∈ RJα×N, [diag(...wi...)] ∈ RN×N, [Φ] ∈ RJα×N.
Solving this system, obtain vector of coefﬁcients (...ϕβ...)T for
all β.
Finally, the assimilated parameter qa will be
qa = qf + ˜ϕ(ˆy) − ˜ϕ(z), (2)
z(ξ) = y(ξ) + ε(ω), ˜ϕ = β∈Jp
ϕβΦβ(z(ξ))
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Explanation of ” Bayesian Update surrogate” from E. Zander
Let the stochastic model of the measurement is given by
y = M(q) + ε, ε -measurement noise (3)
Best estimator ˜ϕ for q given z, i.e.
˜ϕ = argminϕ E[ q(·) − ϕ(z(·)) 2
2]. (4)
The best estimate (or predictor) of q given the
measurement model is
qM(ξ) = ˜ϕ(z(ξ))). (5)
The remainder, i.e. the difference between q and qM, is
given by
q⊥
M(ξ) = q(ξ) − qM(ξ), (6)
Due to the minimisation property of the MMSE
estimator—orthogonal to qM(ξ), i.e. cov(q⊥
M, qM) = 0.
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In other words,
q(ξ) = qM(ξ) + q⊥
M(ξ) (7)
yields an orthogonal decomposition of q.
Actual measurement ˆy, prediction ˆq = ˜ϕ(ˆy). Part qM of q
can be “collapsed” to ˆq. Updated stochastic model q is
thus given by
q (ξ) = ˆq + q⊥
M(ξ) (8)
q (ξ) = q(ξ) + ( ˜ϕ(ˆy) − ˜ϕ(z(ξ))). (9)
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Example: 1D elliptic PDE with uncertain coeffs
− · (κ(x, ξ) u(x, ξ)) = f(x, ξ), x ∈ [0, 1]
+ Dirichlet random b.c. g(0, ξ) and g(1, ξ).
3 measurements: u(0.3) = 22, s.d. 0.2, x(0.5) = 28, s.d. 0.3,
x(0.8) = 18, s.d. 0.3.
κ(x, ξ): N = 100 dofs, M = 5, number of KLE terms 35, beta distribution for κ, Gaussian covκ, cov.
length 0.1, multi-variate Hermite polynomial of order pκ = 2;
RHS f(x, ξ): Mf = 5, number of KLE terms 40, beta distribution for κ, exponential covf , cov. length 0.03,
multi-variate Hermite polynomial of order pf = 2;
b.c. g(x, ξ): Mg = 2, number of KLE terms 2, normal distribution for g, Gaussian covg , cov. length 10,
multi-variate Hermite polynomial of order pg = 1;
pφ = 3 and pu = 3
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Example: updating of the solution u
0 0.5 1
-20
0
20
40
60
0 0.5 1
-20
0
20
40
60
Figure: Original and updated solutions, mean value plus/minus 1,2,3
standard deviations
[graphics are built in the stochastic Galerkin library sglib, written by E. Zander in TU Braunschweig]
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Example: Updating of the parameter
0 0.5 1
0
0.5
1
1.5
0 0.5 1
0
0.5
1
1.5
Figure: Original and updated parameter κ.
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Part III. Tensor completion
Now, we consider how to
apply Tensor Completion Techniques
for Bayesian Update
In Bayesian Update surrogate, the assimilated PCE coeffs of
parameter qa will be
NEW gPCE coeffs=OLD gPCE coeffs + gPCE of Update
ALL INGREDIENTS ARE TENSORS!
qa = qf + ˜ϕ(ˆy) − ˜ϕ(z), (10)
z(ξ) = y(ξ) + ε(ω), qa ∈ RN×#Ja , N = 1..107, #Ja > 1000,
#Jf < #Ja.

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Problem setup: Tensor completion
Problem of fitting a low rank tensor A ∈ RI, I := I1 × ... × Id ,
Iµ = {1, ..., nµ}, µ ∈ D := {1, .., d}, to given data points
{Mi ∈ R | i ∈ P}, P ⊂ I, #P ≥
d
µ=1
nµ, (11)
by minimizing the distance between the given values (Mi)i∈P
and approximations (Ai)i∈P:
A = argminÃ∈T
i∈P
(Mi − Ãi)2
(12)
Remark: here we assume that our target tensor M allows for a
low rank approximation M − ˜M ≤ ε, ε ≥ 0 and ˜M fulfills
certain rank bounds, T - Low rank format under consideration.
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Problem setup: Tensor completion
L. Grasedyck et all, 2016, hierarchical and tensor train formats
W. Austin, T, Kolda, D, Kressner, M. Steinlechner et al, CP
format
Goal: Reconstruct tensor with O(log N) number of samples.
Methods:
1. ALS inspired by LMaFit method for matrix completion,
complexity O(r4d#P).
2. Alternating directions ﬁtting (ADF), complexity O(r2d#P).
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Numerical experiments for SPDEs: Tensor completion
[L. Grasedyck, M. Kluge, S. Kraemer, SIAM J. Sci. Comput., Vol 37/5, 2016]
Applied ALS and ADF methods to:
− div(κ(x, ω) u(x, ω)) = 1 in D × Ω,
u(x, ω) = 0 on ∂G × Ω,
(13)
D = [−1, 1]. The goal is to determine u(ω) := D u(x, ω)dx.
FE with 50 dofs, KLE with d terms, d-stochastic independent
RVs,
Yields to tensor Ai1...id
:= u(i1, ..., id ),
n = 100, d = 5, slice density CSD = 6.
Software (matlab) is available.
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Example: updating of the solution u
0 0.5 1
-20
0
20
40
60
0 0.5 1
-20
0
20
40
60
0 0.5 1
-20
0
20
40
60
0 0.5 1
-20
0
20
40
60
0 0.5 1
-20
0
20
40
60
Figure: Original and updated solutions, mean value plus/minus 1,2,3
standard deviations. Number of available measurements {0, 1, 2, 3, 5}
[graphics are built in the stochastic Galerkin library sglib, written by E. Zander in TU Braunschweig]
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Conclusion
Introduced low-rank tensor methods to solve elliptic PDEs
with uncertain coefﬁcients,
Explained how to compute the maximum and the mean in
low-rank tensor format,
Derived Bayesian update surrogate ϕ (as a linear,
quadratic, cubic etc approximation), i.e. compute
conditional expectation of q, given measurement y.
Apply Tensor Completion method to sparse measurement
tensor in the likelihood.
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Tensor completion for PDEs with uncertain coefficients and Bayesian update surrogate

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