Robust sparse linear regression for tokamak plasma boundary estimation using variational Bayes

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61389021%3A_____%2F18%3A00491728" target="_blank" >RIV/61389021:_____/18:00491728 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985556:_____/18:00491728 RIV/68407700:21230/18:00324830 RIV/68407700:21340/18:00324830

Výsledek na webu

<a href="http://dx.doi.org/10.1088/1742-6596/1047/1/012015" target="_blank" >http://dx.doi.org/10.1088/1742-6596/1047/1/012015</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1088/1742-6596/1047/1/012015" target="_blank" >10.1088/1742-6596/1047/1/012015</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Robust sparse linear regression for tokamak plasma boundary estimation using variational Bayes

Popis výsledku v původním jazyce

Precise control of the shape of plasma in a tokamak requires reliable reconstruction of the plasma boundary. The problem of boundary estimation can be reduced to a simple linear regression with a potentially infinite amount of regressors. This regression problem poses some difficulties for classical methods. The selection of regressors significantly influences the reconstructed boundary. Also, the underlying model may not be valid during certain phases of the plasma discharge. Formal model structure estimation technique based on the automatic relevance principle yields a version of sparse least squares estimator. In this contribution, we extend the previous method by relaxing the assumption of Gaussian noise and using Student’s t-distribution instead. Such a model is less sensitive to potential outliers in the measurement. We show on simulations and real data that the proposed modification improves estimation of the plasma boundary in some stages of a plasma discharge. Performance of the resulting algorithm is evaluated with respect to a more detailed and computationally costly model which is considered to be the „ground truth“. The results are also compared to those of Lasso and Tikhonov regularization techniques.

Název v anglickém jazyce

Robust sparse linear regression for tokamak plasma boundary estimation using variational Bayes

Popis výsledku anglicky

Precise control of the shape of plasma in a tokamak requires reliable reconstruction of the plasma boundary. The problem of boundary estimation can be reduced to a simple linear regression with a potentially infinite amount of regressors. This regression problem poses some difficulties for classical methods. The selection of regressors significantly influences the reconstructed boundary. Also, the underlying model may not be valid during certain phases of the plasma discharge. Formal model structure estimation technique based on the automatic relevance principle yields a version of sparse least squares estimator. In this contribution, we extend the previous method by relaxing the assumption of Gaussian noise and using Student’s t-distribution instead. Such a model is less sensitive to potential outliers in the measurement. We show on simulations and real data that the proposed modification improves estimation of the plasma boundary in some stages of a plasma discharge. Performance of the resulting algorithm is evaluated with respect to a more detailed and computationally costly model which is considered to be the „ground truth“. The results are also compared to those of Lasso and Tikhonov regularization techniques.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10305 - Fluids and plasma physics (including surface physics)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Journal of Physics: Conference Series

ISBN

—

ISSN

1742-6596

e-ISSN

—

Počet stran výsledku

12

Strana od-do

—

Název nakladatele

IOP Publishing Ltd

Místo vydání

Bristol

Místo konání akce

Waterloo

Datum konání akce

23. 5. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—