Robust Bayesian linear regression for Tokamak plasma boundary … · 2017-06-15 · Robust Bayesian linear regression for Tokamak plasma boundary estimation Vít Škvára 1 ;2, Jakub

Robust Bayesian linear regression for Tokamakplasma boundary estimation

Vít Škvára1,2, Jakub Urban1, Václav Šmídl2,3

1Institute of Plasma Physics, Prague, Czech Republic2Institute of Information Theory and Automation, Prague, Czech Republic

3Regional Innovation Center for Electrical Engineering, Pilsen, Czech Republic

2nd IAEA Technical Meeting on Fusion Data Processing, Validation andAnalysis / May 30, 2017

V. Škvára: Bayesian linear regression for boundary estimation / 2nd IAEA TM FDPVA / May 19, 2017 1/16

Outline

1 Toroidal harmonics equilibrium model

2 Bayesian formulation of the regression problem

3 Results evaluation

4 Conclusion

[1] Faugeras, B. et al. 2D interpolation and extrapolation of discrete magneticmeasurements with toroidal harmonics for equilibrium reconstruction in aTokamak. Plasma Physics and Controlled Fusion. 2014, 56.


Magnetic flux in a tokamak

0.3 0.4 0.5 0.6 0.7 0.8

r [m]

−0.4

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0.0

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z [m

]

ψ

chamber wall

magnetic probe

We seek a form of magnetic flux ψ, that satisfies partial differential equation

−∆∗ψ = 0 in domain D,ψ = ψ0 on ∂D,

where D is a domain without plasma or other current source.


1 There is a known solution, given by decomposition into toroidalharmonics series:

ψ(r , z) =∞∑

n=0

unF (n, r , z)

where F (.) are known functions (containing the toroidal harmonics),un ∈ R are unknown coefficients.

2 Knowledge of measurements ψ(r , z) + selection of a finite number ofelements of the decomposition⇒ inverse problem of coefficientsdetermination.

3 With vector u, we reconstruct ψ(r , z)⇒ boundary is identified as lastclosed flux surface.



ψ(r , z) =∞∑

n=0

unF (n, r , z)






ψ(r , z) =∞∑

n=0

unF (n, r , z)





Problem formulation

The previous transforms into a linear model

Y = Xu + e,

whereY are magnetic flux measurements from coils,X is regressor matrix given by F (n, r , z),u is a vector of unknown coefficients,e is noise.

Optimal choice of the degree of decomposition (length of u) is unknown butthe result is extremely sensitive to it.

Proposed solutionConstruction of a hierarchical probability model that

automatically selects the proper decomposition modelis robust to outliers in measurements.


Problem formulation

The previous transforms into a linear model

Y = Xu + e,

whereY are magnetic flux measurements from coils,X is regressor matrix given by F (n, r , z),u is a vector of unknown coefficients,e is noise.

Optimal choice of the degree of decomposition (length of u) is unknown butthe result is extremely sensitive to it.

Proposed solutionConstruction of a hierarchical probability model that

automatically selects the proper decomposition modelis robust to outliers in measurements.


Least squares approach

The modelY = Xu + e,e ∼ N (e|0, β−1I)

leads to the likelihood function

p(Y |u, β) = N (Y |Xu, β−1I).

An estimate of u is obtained by maximizing the likelihood which results in theclosed-form solution

u = (X T X )−1X T Y .

How to extend this?





p(Y |u, β) = N (Y |Xu, β−1I).


u = (X T X )−1X T Y .

How to extend this?





p(Y |u, β) = N (Y |Xu, β−1I).


u = (X T X )−1X T Y .

How to extend this?


Hierarchical model 1 - LS-ARD

Now begins the construction of a hierarchical probability model. So far wehave

p(Y |u, β) = N (Y |Xu, β−1I).

Now, we add a prior distribution on the vector u:

p(u|α) = N (u|0,diag(α−1)).

Also, we add a gamma prior on both precisions parameters:

p(β) = G(β|a0,b0),

p(αi ) = G(αi |c0,d0).

Now, u, β, α are unknown. How to estimate them?




p(Y |u, β) = N (Y |Xu, β−1I).


p(u|α) = N (u|0,diag(α−1)).


p(β) = G(β|a0,b0),

p(αi ) = G(αi |c0,d0).





p(Y |u, β) = N (Y |Xu, β−1I).


p(u|α) = N (u|0,diag(α−1)).


p(β) = G(β|a0,b0),

p(αi ) = G(αi |c0,d0).



Variational Bayes

We must estimate the posterior distribution p(u, β, α|Y ) = distribution ofparameters given the data Y . For that, we use the variational Bayesframework. We approximate the true posterior with a mixture of marginals

p(u, β, α|Y ) ≈ p(u, β, α|Y ) = p(u|Y )p(β|Y )p(α|Y ).

What does this result into?

We obtain an iterative algorithm that computes hyperparameters (means andvariances) of the posterior distributions.

In effect, the iterative algorithm solves the regularized least squares problem

minu

∑i

(Yi − Xiu)2 +∑

j

αju2j

with adaptive αj .


Variational Bayes

We must estimate the posterior distribution p(u, β, α|Y ) = distribution ofparameters given the data Y . For that, we use the variational Bayesframework. We approximate the true posterior with a mixture of marginals

p(u, β, α|Y ) ≈ p(u, β, α|Y ) = p(u|Y )p(β|Y )p(α|Y ).

What does this result into?

We obtain an iterative algorithm that computes hyperparameters (means andvariances) of the posterior distributions.

In effect, the iterative algorithm solves the regularized least squares problem

minu

∑i

(Yi − Xiu)2 +∑

j

αju2j

with adaptive αj .


Sparsity of the model

Equations of the posterior distribution moments are solved iteratively.By estimating α - the precision (inverse of variance) of u ⇒ less informativeelements are forced to zero.

1 30 60

N

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−0.01

0.00

0.01

ui

i=1

i=4

i=6

1 30 60

N

103

106

109

αi

Figure : Convergence of elements of ui and their respective precisions αi .


Hierarchical model 2-RLS-ARD

To achieve robustness, we assume that the noise has the Student’s tdistribution

p(Y |u, ω, z) = N(

Y |Xu, (ω diag(z))−1),

p(ω) = G(ω|ρ0, κ0),

p(zi |d) = G(z|d/2,d/2),

p(d) = G(d |ψ0, φ0),

which follows from∫N (e|0, (ω diag(z))−1)G(z|d/2,d/2)dz = S(e|0, ω,d).

Again, the iterative algorithm solves the problem

minu

∑i

√zi (Yi − Xiu)2 +

∑j

αju2j

with adaptive zi , αj .


Hierarchical model 2-RLS-ARD

To achieve robustness, we assume that the noise has the Student’s tdistribution

p(Y |u, ω, z) = N(

Y |Xu, (ω diag(z))−1),

p(ω) = G(ω|ρ0, κ0),

p(zi |d) = G(z|d/2,d/2),

p(d) = G(d |ψ0, φ0),

which follows from∫N (e|0, (ω diag(z))−1)G(z|d/2,d/2)dz = S(e|0, ω,d).

Again, the iterative algorithm solves the problem

minu

∑i

√zi (Yi − Xiu)2 +

∑j

αju2j

with adaptive zi , αj .


Outlier detection

Estimate of d represents normality of the measurements, whilemeasurements with zi < 1 are regarded as outliers.

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t [s]

10

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24d

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t [s]

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1.10z

Figure : Time evolution of d and zi during a discharge.


Validation

COMPASS shot number 9339.Ground truth data simulated with the EFIT++ equilibrium reconstructioncode.The second model was updated to distinguish between flux loops andmagnetic probes.


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sim

ula

ted d

ata

r

[m

]

t = 1.02 s t = 1.13 s

limiter

EFIT++

OLS Student2 measurement point

t = 1.21 s

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z [m]

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real m

easu

rem

ents

r

[m

]

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z [m]0.3 0.4 0.5 0.6 0.7 0.8

z [m]

Figure : Boundary shapes computed with EFIT++ (ground truth), original model (OLS)and Student’s t noise models. Enlarged circles depict "outlying" measurements.


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MSE

simulated data

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0.35measured data

Student1

Student2

Gauss

OLS

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t [s]

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DH

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Figure : Fit MSE (upper row) and Hausdorff distance DH for different estimationmethods during a discharge.


EFIT++

It solves the Grad-Shafranov equation

jp(ψ, r) = rp′ +1µ0r

(ff ′)(ψ)

for unknown functions p′, ff ′.These are approximated with a decomposition into polynomial, spline orChebyshev basis functions.With a proper model, we may extend the base and achieve robustness.


Conclusion

We have presented a Bayesian approach to linear regression.Universal algorithm with self-tuning regularization and robustnessparameters.By modeling prior knowledge, we improved the results of boundaryidentification.Outlook - implementation of the method into EFIT++ code.


Documents

Robust Bayesian linear regression for Tokamak plasma boundary … · 2017-06-15 · Robust Bayesian linear regression for Tokamak plasma boundary estimation Vít Škvára 1 ;2, Jakub