Synthetic data generation using generative adversarial network for tokamak plasma current quench experiments
出版年份 2022 全文链接
标题
Synthetic data generation using generative adversarial network for tokamak plasma current quench experiments
作者
关键词
-
出版物
CONTRIBUTIONS TO PLASMA PHYSICS
Volume -, Issue -, Pages -
出版商
Wiley
发表日期
2022-12-03
DOI
10.1002/ctpp.202200051
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Disruption prediction at JET through Deep Convolutional Neural Networks using spatiotemporal information from plasma profiles
- (2022) Enrico Aymerich et al. NUCLEAR FUSION
- Prospects for Disruption Handling in a Tokamak-Based Fusion Reactor
- (2021) N. W. Eidietis FUSION SCIENCE AND TECHNOLOGY
- Real-time prediction of high density EAST disruptions using Random Forest
- (2021) Wenhui Hu et al. NUCLEAR FUSION
- An Application of Machine Learning for Plasma Current Quench Studies via Synthetic Data Generation
- (2021) Niharika Dalsania et al. FUSION ENGINEERING AND DESIGN
- Progress Toward Interpretable Machine Learning-Based Disruption Predictors Across Tokamaks
- (2020) C. Rea et al. FUSION SCIENCE AND TECHNOLOGY
- Characterization of the plasma current quench during disruptions in ADITYA tokamak
- (2020) Shishir Purohit et al. NUCLEAR FUSION
- Disruption prediction using a full convolutional neural network on EAST
- (2020) B H Guo et al. PLASMA PHYSICS AND CONTROLLED FUSION
- Predicting disruptive instabilities in controlled fusion plasmas through deep learning
- (2019) Julian Kates-Harbeck et al. NATURE
- Machine learning for disruption warning on Alcator C-Mod, DIII-D, and EAST
- (2019) Kevin Joseph Montes et al. NUCLEAR FUSION
- A disruption predictor based on a 1.5-dimensional convolutional neural network in HL-2A
- (2019) Zongyu Yang et al. NUCLEAR FUSION
- Deep Learning for Plasma Tomography and Disruption Prediction From Bolometer Data
- (2019) Diogo R. Ferreira et al. IEEE TRANSACTIONS ON PLASMA SCIENCE
- Exploratory Machine Learning Studies for Disruption Prediction Using Large Databases on DIII-D
- (2018) Cristina Rea et al. FUSION SCIENCE AND TECHNOLOGY
- Disruption prediction investigations using Machine Learning tools on DIII-D and Alcator C-Mod
- (2018) C Rea et al. PLASMA PHYSICS AND CONTROLLED FUSION
- Deep Gaussian mixture models
- (2017) Cinzia Viroli et al. STATISTICS AND COMPUTING
- Prediction of density limit disruptions on the J-TEXT tokamak
- (2016) S Y Wang et al. PLASMA PHYSICS AND CONTROLLED FUSION
- Analysis of Similarity Measures in Times Series Clustering for the Discovery of Building Energy Patterns
- (2013) Félix Iglesias et al. Energies
- Characteristics of current quenches during disruptions in the J-TEXT tokamak
- (2012) Y Zhang et al. PHYSICA SCRIPTA
- Theory of tokamak disruptions
- (2012) Allen H. Boozer PHYSICS OF PLASMAS
- Study of current decay time during disruption in JT-60U tokamak
- (2010) Y. Shibata et al. NUCLEAR FUSION
- Characterization of the plasma current quench during disruptions in the National Spherical Torus Experiment
- (2009) S.P. Gerhardt et al. NUCLEAR FUSION
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now