GS-DeepNet: mastering tokamak plasma equilibria with deep neural networks and the Grad–Shafranov equation

to related objects

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: 1 Online-Ressource.

Language: Englisch

Bibliographic citation: GS-DeepNet: mastering tokamak plasma equilibria with deep neural networks and the Grad–Shafranov equation ; volume:13 ; number:1 ; day:22 ; month:9 ; year:2023 ; pages:1-15 ; date:12.2023
Scientific reports ; 13, Heft 1 (22.9.2023), 1-15, 12.2023

Creator: Joung, Semin
Ghim, Y.-C
Kim, Jaewook
Kwak, Sehyun
Kwon, Daeho
Sung, C.
Kim, D.
Kim, Hyun-Seok
Bak, J. G.
Yoon, S. W.

Contributor: SpringerLink (Online service)

DOI: 10.1038/s41598-023-42991-5

URN: urn:nbn:de:101:1-2024020907500750640432

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:31 AM CEST

Data provider

This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.

Show original at data provider

Associated

Joung, Semin
Ghim, Y.-C
Kim, Jaewook
Kwak, Sehyun
Kwon, Daeho
Sung, C.
Kim, D.
Kim, Hyun-Seok
Bak, J. G.
Yoon, S. W.
SpringerLink (Online service)

Other Objects (12)

Mastering the Game of Abalone using Deep Reinforcement Learning and Self-Play

Computational Performance of Deep Reinforcement Learning to Find Nash Equilibria

Correction to: Computational Performance of Deep Reinforcement Learning to Find Nash Equilibria

Modeling of Solid−Liquid Equilibria in Deep Eutectic Solvents: A Parameter Study

Akten

TOKAMAK

Akten

TOKAMAK

Akten

TOKAMAK

Aufsatzsammlung

Deep reading, deep learning

Ansichtskarte

Hugo Haase's Deep to Deep

Understanding deep eutectic systems from solid–liquid equilibria perspective: Experimental study and thermodynamic modeling

Abschnitt

Deep

Graffiti | Streetart

DEEPS

Mastering the Game of Abalone using Deep Reinforcement Learning and Self-Play

Computational Performance of Deep Reinforcement Learning to Find Nash Equilibria

Correction to: Computational Performance of Deep Reinforcement Learning to Find Nash Equilibria

Modeling of Solid−Liquid Equilibria in Deep Eutectic Solvents: A Parameter Study

Akten

TOKAMAK

Akten

TOKAMAK

Akten

TOKAMAK

Aufsatzsammlung

Deep reading, deep learning

Ansichtskarte

Hugo Haase's Deep to Deep

Understanding deep eutectic systems from solid–liquid equilibria perspective: Experimental study and thermodynamic modeling

Abschnitt

Deep

Graffiti | Streetart

DEEPS

Mastering the Game of Abalone using Deep Reinforcement Learning and Self-Play

Computational Performance of Deep Reinforcement Learning to Find Nash Equilibria

Correction to: Computational Performance of Deep Reinforcement Learning to Find Nash Equilibria

Modeling of Solid−Liquid Equilibria in Deep Eutectic Solvents: A Parameter Study

Akten

TOKAMAK

Akten

TOKAMAK

Akten

TOKAMAK

Aufsatzsammlung

Deep reading, deep learning

Ansichtskarte

Hugo Haase's Deep to Deep

Understanding deep eutectic systems from solid–liquid equilibria perspective: Experimental study and thermodynamic modeling

Abschnitt

Deep

Graffiti | Streetart

DEEPS

Cultural heritage institutions wishing to register will find more information here.

Fields marked * need to be filled in.

Username*

Please enter your username

Email*

Please enter your email address

Please do not fill this field

First name

Last name

Password*

Please enter your password

Confirm password*

Please enter the same password

I have read the terms of use and the privacy policy for the collection of personal data and accept them. *

This field is required.

I would like to subscribe to the newsletter of the Deutsche Digitale Bibliothek. See newsletter subscription info.

Account created

Your "My DDB" account has been successfully created. Before you can log in to your account, you must click the confirmation link in the message we just sent to the email address you provided.

GS-DeepNet: mastering tokamak plasma equilibria with deep neural networks and the Grad–Shafranov equation

Object Details

References and Relationships

Contributors, Places and Time

Further information

Data provider

Associated

Other Objects (12)

Mastering the Game of Abalone using Deep Reinforcement Learning and Self-Play

Computational Performance of Deep Reinforcement Learning to Find Nash Equilibria

Correction to: Computational Performance of Deep Reinforcement Learning to Find Nash Equilibria

Modeling of Solid−Liquid Equilibria in Deep Eutectic Solvents: A Parameter Study

TOKAMAK

TOKAMAK

TOKAMAK

Deep reading, deep learning

Hugo Haase's Deep to Deep

Understanding deep eutectic systems from solid–liquid equilibria perspective: Experimental study and thermodynamic modeling

Deep

DEEPS

Mastering the Game of Abalone using Deep Reinforcement Learning and Self-Play

Computational Performance of Deep Reinforcement Learning to Find Nash Equilibria

Correction to: Computational Performance of Deep Reinforcement Learning to Find Nash Equilibria

Modeling of Solid−Liquid Equilibria in Deep Eutectic Solvents: A Parameter Study

TOKAMAK

TOKAMAK

TOKAMAK

Deep reading, deep learning

Hugo Haase's Deep to Deep

Understanding deep eutectic systems from solid–liquid equilibria perspective: Experimental study and thermodynamic modeling

Deep

DEEPS

Mastering the Game of Abalone using Deep Reinforcement Learning and Self-Play

Computational Performance of Deep Reinforcement Learning to Find Nash Equilibria

Correction to: Computational Performance of Deep Reinforcement Learning to Find Nash Equilibria

Modeling of Solid−Liquid Equilibria in Deep Eutectic Solvents: A Parameter Study

TOKAMAK

TOKAMAK

TOKAMAK

Deep reading, deep learning

Hugo Haase's Deep to Deep

Understanding deep eutectic systems from solid–liquid equilibria perspective: Experimental study and thermodynamic modeling

Deep

DEEPS

Related objects

Reset password