C. Rea

发表

Overview of the RFX-mod contribution to the international Fusion Science Program

W. A. Cooper, L. Giudicotti, E. Zilli, 2015 .

Implementation of AI/Deep Learning Disruption Predictor into a Plasma Control System

Kyle G. Felker, Zhihong Lin, B. Grierson, 2022, 2204.01289.

PSFC/JA-21-6 Disruption Prevention via Interpretable Data-Driven Algorithms On DIII-D and EAST

C. Rea, J. Barr, R. Granetz, 2021 .

3D effects on the RFX-mod boundary

G. Spizzo, G. Masi, R. Cavazzana, 2013 .

The (18O,16O) two-neutron transfer reaction at 84 MeV

A. Cunsolo, C. Rea, A. Foti, 2012 .

New structures in the continuum of 15C populated by two-neutron transfer

A. Cunsolo, C. Rea, A. Foti, 2012 .

The phenomenology of reconnection events in the reversed field pinch

H. Isliker, P. Zanca, R. Cavazzana, 2020, Nuclear Fusion.

3D physics, turbulence and transport in the plasma boundary of RFX-mod

C. Rea, 2015 .

Magnetic perturbations as a viable tool for edge turbulence modification

L. Marrelli, G. Spizzo, P. Zanca, 2014 .

Overview of the SPARC tokamak

P. Snyder, M. Greenwald, F. Sciortino, 2020, Journal of Plasma Physics.

MHD stability and disruptions in the SPARC tokamak

R. A. Tinguely, M. Greenwald, R. Granetz, 2020, Journal of Plasma Physics.

Comparative studies of electrostatic turbulence induced transport in presence of resonant magnetic perturbations in RFX-mod

G. Spizzo, G. Masi, R. Cavazzana, 2015 .

Overview of the RFX-mod fusion science activity

G. Pucella, Faa Federico Felici, P. Vincenzi, 2017 .

Highlights from the community white paper “Enhancing US fusion science with data-centric technologies”

David R. Smith, Cristina Rea, Robert S. Granetz, 2018 .

PSFC / RR-19-6 Confinement regime identification on Alcator C-Mod using supervised machine learning methods

A. Hubbard, S. Wolfe, D. Whyte, 2019 .

DIII-D research advancing the physics basis for optimizing the tokamak approach to fusion energy

C. Kamath, R. Churchill, D. Elder, 2021, Nuclear Fusion.

Confinement mode identification of fusion plasmas utilizing machine learning methods

Cristina Rea, A. E. Hubbard, Jerry Hughes, 2018 .

Nested cross-validation loop for performance optimization in imbalanced problems

Kevin Montes, Cristina Rea, Robert S. Granetz, 2018 .

A Machine Learning-based Real Time Disruption Predictor on DIII-D

N. W. Eidietis, Kevin Montes, Cristina Rea, 2018 .

Disruption prediction: from shallow to deep learning and interpretability techniques

Kevin Montes, Cristina Rea, Robert S. Granetz, 2018 .

Data augmentation for disruption prediction via robust surrogate models

K. Rath, C. Albert, C. Rea, 2022, Journal of Plasma Physics.

Hybrid deep-learning architecture for general disruption prediction across multiple tokamaks

R. A. Tinguely, R. Sweeney, C. Rea, 2020, Nuclear Fusion.

A real-time machine learning-based disruption predictor in DIII-D

Kevin Montes, Cristina Rea, Robert S. Granetz, 2019, Nuclear Fusion.

Progress Toward Interpretable Machine Learning–Based Disruption Predictors Across Tokamaks

Cristina Rea, Robert Granetz, Kevin Montes, 2020 .

Scenario adaptive disruption prediction study for next generation burning-plasma tokamaks

R. Sweeney, O. Meneghini, R. A. Tinguely, 2021, Nuclear Fusion.

Overview of the SPARC physics basis towards the exploration of burning-plasma regimes in high-field, compact tokamaks

J. Rice, M. Greenwald, D. Whyte, 2022, Nuclear Fusion.

Real-time prediction of high-density EAST disruptions using random forest

R. Granetz, Y. Huang, J. Qian, 2021, Nuclear Fusion.

Machine learning for disruption warnings on Alcator C-Mod, DIII-D, and EAST

N. W. Eidietis, Orso Meneghini, Mark D. Boyer, 2019, Nuclear Fusion.

Disruption prediction on EAST tokamak using a deep learning algorithm

R. Granetz, L. Zeng, C. Rea, 2021, Plasma Physics and Controlled Fusion.

Disruption prediction investigations using Machine Learning tools on DIII-D and Alcator C-Mod

N. W. Eidietis, Kevin Montes, Cristina Rea, 2018, Plasma Physics and Controlled Fusion.

Exploratory Machine Learning Studies for Disruption Prediction Using Large Databases on DIII-D

Cristina Rea, Robert S. Granetz, R. Granetz, 2018 .

Disruption prediction using a full convolutional neural network on EAST

Heng Zhang, Cristina Rea, Jinping Qian, 2020, Plasma Physics and Controlled Fusion.

Toward active disruption avoidance via real-time estimation of the safe operating region and disruption proximity in tokamaks

M. Boyer, C. Rea, M. Clement, 2021, Nuclear Fusion.

The Impact of Disruptions on the Economics of a Tokamak Power Plant

E. Marmar, C. Rea, R. Granetz, 2023, Fusion Science and Technology.

Development and experimental qualification of novel disruption prevention techniques on DIII-D

M. Boyer, D. Humphreys, A. Hyatt, 2021, Nuclear Fusion.

Empirical probability and machine learning analysis of m, n = 2, 1 tearing mode onset parameter dependence in DIII-D H-mode scenarios

C. Rea, N. Logan, L. Bardoczi, 2023, Physics of Plasmas.

Disruption prediction on EAST with different wall conditions based on a multi-scale deep hybrid neural network

L. Zeng, C. Rea, R. Granetz, 2023, Nuclear Fusion.

Implementation of AI/DEEP learning disruption predictor into a plasma control system

Zhihong Lin, B. Grierson, R. Nazikian, 2022, Contributions to Plasma Physics.

A semi-supervised machine learning detector for physics events in tokamak discharges

R. A. Tinguely, R. Granetz, R. Sweeney, 2020 .