Detection of Alfvén Eigenmodes on COMPASS with Generative Neural Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61389021%3A_____%2F20%3A00538109" target="_blank" >RIV/61389021:_____/20:00538109 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985556:_____/20:00538109 RIV/68407700:21230/20:00344433 RIV/68407700:21340/20:00344433

Výsledek na webu

<a href="https://www.tandfonline.com/doi/pdf/10.1080/15361055.2020.1820805?needAccess=true&" target="_blank" >https://www.tandfonline.com/doi/pdf/10.1080/15361055.2020.1820805?needAccess=true&</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/15361055.2020.1820805" target="_blank" >10.1080/15361055.2020.1820805</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Detection of Alfvén Eigenmodes on COMPASS with Generative Neural Networks

Popis výsledku v původním jazyce

Chirping Alfvén eigenmodes were observed at the COMPASS tokamak. They are believed to be driven by runaway electrons (REs), and as such, they provide a unique opportunity to study the physics of nonlinear interaction between REs and electromagnetic instabilities, including important topics of RE mitigation and losses. On COMPASS, they can be detected from spectrograms of certain magnetic probes. So far, their detection has required much manual effort since they occur rarely. We strive to automate this process using machine learning techniques based on generative neural networks. We present two different models that are trained using a smaller, manually labeled database and a larger unlabeled database from COMPASS experiments. In a number of experiments, we demonstrate that our approach is a viable option for automated detection of rare instabilities in tokamak plasma.

Název v anglickém jazyce

Detection of Alfvén Eigenmodes on COMPASS with Generative Neural Networks

Popis výsledku anglicky

Chirping Alfvén eigenmodes were observed at the COMPASS tokamak. They are believed to be driven by runaway electrons (REs), and as such, they provide a unique opportunity to study the physics of nonlinear interaction between REs and electromagnetic instabilities, including important topics of RE mitigation and losses. On COMPASS, they can be detected from spectrograms of certain magnetic probes. So far, their detection has required much manual effort since they occur rarely. We strive to automate this process using machine learning techniques based on generative neural networks. We present two different models that are trained using a smaller, manually labeled database and a larger unlabeled database from COMPASS experiments. In a number of experiments, we demonstrate that our approach is a viable option for automated detection of rare instabilities in tokamak plasma.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

Fusion Science and Technology

ISSN

1536-1055

e-ISSN

—

Svazek periodika

76

Číslo periodika v rámci svazku

8

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

10

Strana od-do

962-971

Kód UT WoS článku

000586815000001

EID výsledku v databázi Scopus

2-s2.0-85095722107