After we applied the stochastic Galerkin method to solve stochastic PDE, and solve large linear system, we obtain stochastic solution (random field), which is represented in Karhunen Loeve and PCE basis. No sampling error is involved, only algebraic truncation error. Now we would like to escape classical MCMC path to compute the posterior. We develop an Bayesian* update formula for KLE-PCE coefficients.

1. NON-LINEAR APPROXIMATION OF
BAYESIAN UPDATE
Alexander Litvinenko1, Hermann G. Matthies2, Elmar Zander2
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http://sri-uq.kaust.edu.sa/
1Extreme Computing Research Center, KAUST,
2Institute of Scientific Computing, TU Braunschweig, Brunswick, Germany

2. 4*
KAUST
Figure : KAUST campus, 7 years old, approx. 7000 people (include
1700 kids), 100 nations, 36 km2
.
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3. 4*
Stochastic Numerics Group at KAUST
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5. 4*
A BIG picture
1. Computing the full Bayesian update is very expensive
(MCMC is expensive)
2. Look for a cheap surrogate (linear, quadratic, cubic,...
approx.)
3. Kalman filter is a particular case
4. Do Bayesian update of Polynomial Chaos Coefficients!
(not probability densities!)
5. Consider non-Gaussian cases
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6. 4*
History
1. O. Pajonk, B. V. Rosic, A. Litvinenko, and H. G. Matthies, A
Deterministic Filter for Non-Gaussian Bayesian Estimation,
Physica D: Nonlinear Phenomena, Vol. 241(7), pp. 775-788,
2012.
2. B. V. Rosic, A. Litvinenko, O. Pajonk and H. G. Matthies,
Sampling Free Linear Bayesian Update of Polynomial Chaos
Representations, J. of Comput. Physics, Vol. 231(17), 2012 , pp
5761-5787
3. A. Litvinenko and H. G. Matthies, Inverse problems and
uncertainty quantification
http://arxiv.org/abs/1312.5048, 2013
4. H. G. Matthies, E. Zander, B.V. Rosic, A. Litvinenko, Parameter
estimation via conditional expectation - A Bayesian Inversion,
accepted to AMOS-D-16-00015, 2016.
5. H. G. Matthies, E. Zander, B.V. Rosic, A. Litvinenko, Bayesian
parameter estimation via filtering and functional approximation,
Tehnicki vjesnik 23, 1(2016), 1-17.Center for Uncertainty
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7. 4*
Setting for the identification process
General idea:
We observe / measure a system, whose structure we know in
principle.
The system behaviour depends on some quantities
(parameters),
which we do not know ⇒ uncertainty.
We model (uncertainty in) our knowledge in a Bayesian setting:
as a probability distribution on the parameters.
We start with what we know a priori, then perform a
measurement.
This gives new information, to update our knowledge
(identification).
Update in probabilistic setting works with conditional
probabilities
⇒ Bayes’s theorem.
Repeated measurements lead to better identification.
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8. 4*
Mathematical setup
Consider
A(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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9. 4*
Inverse problem
For given f, measurement y is just a function of q.
This function is usually not invertible ⇒ ill-posed problem,
measurement y does not contain enough information.
In Bayesian framework state of knowledge modelled in a
probabilistic way,
parameters q are uncertain, and assumed as random.
Bayesian setting allows updating / sharpening of information
about q when measurement is performed.
The problem of updating distribution—state of knowledge of q
becomes well-posed.
Can be applied successively, each new measurement y and
forcing f —may also be uncertain—will provide new
information.
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10. 4*
Conditional probability and expectation
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).
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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11. 4*
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)),
and represents new state of knowledge after the measurement.
Doob-Dynkin: Q∞ = {ϕ ∈ Q : ϕ = φ ◦ z, φ measurable}.
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12. 4*
Polynomial Chaos Expansion (Norbert Wiener, 1938)
Multivariate Hermite polynomials were used to approximate
random fields/stochastic processes with Gaussian random
variables. According to Cameron and Martin theorem PCE
expansion converges in the L2 sense.
Let Y(x, θ), θ = (θ1, ..., θM, ...), is approximated:
Y(x, θ) =
β∈Jm,p
Hβ(θ)Yβ(x), |Jm,p| =
(m + p)!
m!p!
,
Hβ(θ) = M
k=1 hβk
(θk ),
Yβ(x) =
1
β! Θ
Hβ(θ)Y(x, θ) P(dθ).
Yβ(x) ≈
1
β!
Nq
i=1
Hβ(θi)Y(x, θi)wi.
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13. 4*
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)) (1)
and minimize q(ξ) − ˜ϕ(z(ξ)) 2, 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 (2)
Inserting representation for ˜ϕ, obtain
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14. 4*
Numerical computation of NLBU
∂
∂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
E [Φβ(z)Φα(z)] ϕβ = E [qΦα(z)]
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15. 4*
Numerical computation of NLBU
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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16. 4*
Numerical computation of NLBU
Using the same quadrature rule of order q for each element of
A, we can write
A = E ΦJα (z(ξ))ΦJβ
(z(ξ))T
≈
NA
i=1
wA
i ΦJα (zi)ΦJβ
(zi)T
, (3)
where (wA
i , ξi) are weights and quadrature points, zi := z(ξi)
and ΦJα (zi) := (...Φα(z(ξi))....)T is a vector of length |Jα|.
b = E [q(ξ)ΦJα (z(ξ))] ≈
Nb
i=1
wb
i q(ξi)ΦJα (zi), (4)
where ΦJα (z(ξi)) := (..., Φα(z(ξi)), ...), α ∈ Jα.
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17. 4*
Numerical computation of NLBU
We can write the Eq. 15 with the right-hand side in Eq. 4 in the
compact form:
[ΦA] [diag(...wA
i ...)] [ΦA]T




...
ϕβ
...



 = [Φb]


wb
0 q(ξ0)
...
wb
Nb q(ξNb )

 (5)
[ΦA] ∈ RJα×NA
, [diag(...wA
i ...)] ∈ RNA×NA
, [Φb] ∈ RJα×Nb
,
[wb
0 q(ξ0)...wb
Nb q(ξNb )] ∈ RNb
.
Solving Eq. 5, obtain vector of coefficients (...ϕβ...)T for all β.
Finally, the assimilated parameter qa will be
qa = qf + ˜ϕ(ˆy) − ˜ϕ(z), (6)
z(ξ) = y(ξ) + ε(ω), ˜ϕ = β∈Jp
ϕβΦβ(z(ξ))
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18. 4*
Example 1: ϕ does not exist in the Hermite basis
Assume z(ξ) = ξ2 and q(ξ) = ξ3. The normalized PCE
coefficients are (1, 0, 1, 0)
(ξ2 = 1 · H0(ξ) + 0 · H1(ξ) + 1 · H2(ξ) + 0 · H3(ξ))
and (0, 3, 0, 1)
(ξ3 = 0 · H0(ξ) + 3 · H1(ξ) + 0 · H2(ξ) + 1 · H3(ξ)).
For such data the mapping ϕ does not exist. The matrix A is
close to singular.
Support of Hermite polynomials (used for Gaussian RVs) is
(−∞, ∞).
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19. 4*
Example 2: ϕ does exist in the Laguerre basis
Assume z(ξ) = ξ2 and q(ξ) = ξ3.
The normalized gPCE coefficients are (2, −4, 2, 0) and
(6, −18, 18, −6).
For such data the mapping mapping ϕ of order 8 and higher
produces a very accurate result.
Support of Laguerre polynomials (used for Gamma RVs) is
[0, ∞).
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20. 4*
Lorenz 1963
Is a system of ODEs. Has chaotic solutions for certain
parameter values and initial conditions.
˙x = σ(ω)(y − x)
˙y = x(ρ(ω) − z) − y
˙z = xy − β(ω)z
Initial state q0(ω) = (x0(ω), y0(ω), z0(ω)) are uncertain.
Solving in t0, t1, ..., t10, Noisy Measur. → UPDATE, solving in
t11, t12, ..., t20, Noisy Measur. → UPDATE,...
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21. Trajectories of x,y and z in time. After each update (new
information coming) the uncertainty drops. (O. Pajonk)
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22. 4*
Lorenz-84 Problem
Figure : Partial state trajectory with uncertainty and three updates
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23. 4*
Lorenz-84 Problem
10 0 10
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
x
20 0 20
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
y
0 10 20
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
z
xf
x
a
yf
y
a
zf
z
a
Figure : NLBU: Linear measurement (x(t), y(t), z(t)): prior and
posterior after one update
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24. 4*
Lorenz-84 Problem
10 5 0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
x
x1
x2
15 10 5
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
y
y1
y2
5 10 15
0
0.1
0.2
0.3
0.4
0.5
0.6
z
z1
z2
Figure : Linear measurement: Comparison posterior for LBU and
NLBU after second update
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25. 4*
Lorenz-84 Problem
20 0 20
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
x
50 0 50
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
y
0 10 20
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
z
xf
x
a
yf
y
a
zf
z
a
Figure : Quadratic measurement (x(t)2
, y(t)2
, z(t)2
): Comparison of
a priori and a posterior for NLBU
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26. 4*
Example 4: 1D elliptic PDE with uncertain coeffs
Taken from Stochastic Galerkin Library (sglib), by Elmar Zander
(TU Braunschweig)
− · (κ(x, ξ) u(x, ξ)) = f(x, ξ), x ∈ [0, 1]
Measurements are taken at x1 = 0.2, and x2 = 0.8. The means
are y(x1) = 10, y(x2) = 5 and the variances are 0.5 and 1.5
correspondingly.
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27. 4*
Example 4: updating of the solution u
0 0.2 0.4 0.6 0.8 1
−20
−10
0
10
20
30
0 0.2 0.4 0.6 0.8 1
−20
−10
0
10
20
30
Figure : Original and updated solutions, mean value plus/minus 1,2,3
standard deviations
See more in sglib by Elmar Zander
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28. 4*
Example 4: Updating of the parameter
0 0.2 0.4 0.6 0.8 1
−1
−0.5
0
0.5
1
1.5
2
0 0.2 0.4 0.6 0.8 1
−1
−0.5
0
0.5
1
1.5
2
Figure : Original and updated parameter q.
See more in sglib by Elmar Zander
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29. 4*
Conclusion about NLBU
+ Step 1. Introduced a way to derive MMSE ϕ (as a linear,
quadratic, cubic etc approximation, i. e. compute
conditional expectation of q, given measurement Y.
Step 2. Apply ϕ to identify parameter q
+ All ingredients can be given as gPC.
+ we apply it to solve inverse problems (ODEs and PDEs).
- Stochastic dimension grows up very fast.
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30. 4*
Acknowledgment
I used a Matlab toolbox for stochastic Galerkin methods (sglib)
https://github.com/ezander/sglib
Alexander Litvinenko and his research work was supported by
the King Abdullah University of Science and Technology
(KAUST), SRI-UQ and ECRC centers.
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31. 4*
Literature
1. Bojana Rosic, Jan Sykora, Oliver Pajonk, Anna Kucerova
and Hermann G. Matthies, Comparison of Numerical
Approaches to Bayesian Updating, report on
www.wire.tu-bs.de, 2014
2. A. Litvinenko and H. G. Matthies, Inverse problems and
uncertainty quantification
http://arxiv.org/abs/1312.5048, 2013
3. L. Giraldi, A. Litvinenko, D. Liu, H. G. Matthies, A. Nouy, To
be or not to be intrusive? The solution of parametric and
stochastic equations - the ”plain vanilla” Galerkin case,
http://arxiv.org/abs/1309.1617, 2013
4. O. Pajonk, B. V. Rosic, A. Litvinenko, and H. G. Matthies, A
Deterministic Filter for Non-Gaussian Bayesian Estimation,
Physica D: Nonlinear Phenomena, Vol. 241(7), pp.
775-788, 2012.
5. B. V. Rosic, A. Litvinenko, O. Pajonk and H. G. Matthies,
Sampling Free Linear Bayesian Update of PolynomialCenter for Uncertainty
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32. 4*
Literature
1. PCE of random coefficients and the solution of stochastic partial
differential equations in the Tensor Train format, S. Dolgov, B. N.
Khoromskij, A. Litvinenko, H. G. Matthies, 2015/3/11,
arXiv:1503.03210
2. Efficient analysis of high dimensional data in tensor formats, M.
Espig, W. Hackbusch, A. Litvinenko, H.G. Matthies, E. Zander Sparse
Grids and Applications, 31-56, 40, 2013
3. Application of hierarchical matrices for computing the
Karhunen-Loeve expansion, B.N. Khoromskij, A. Litvinenko, H.G.
Matthies, Computing 84 (1-2), 49-67, 31, 2009
4. Efficient low-rank approximation of the stochastic Galerkin matrix
in tensor formats, M. Espig, W. Hackbusch, A. Litvinenko, H.G.
Matthies, P. Waehnert, Comp. & Math. with Appl. 67 (4), 818-829,
2012
5. Numerical Methods for Uncertainty Quantification and Bayesian
Update in Aerodynamics, A. Litvinenko, H. G. Matthies, Book
”Management and Minimisation of Uncertainties and Errors in
Numerical Aerodynamics” pp 265-282, 2013
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