Mitsuru Honda, Emi Narita, Shinya Maeyama, Tomo-Hiko Watanabe. Multimodal convolutional neural networks for predicting evolution of gyrokinetic simulations. Contributions to Plasma Physics. 2023. 63. 5-6
Tomonari Nakayama, Motoki Nakata, Mitsuru Honda, Emi Narita, Masanori Nunami, Seikichi Matsuoka. A simplified model to estimate nonlinear turbulent transport by linear dynamics in plasma turbulence. Scientific Reports. 2023. 13. 1
Emi Narita, Mitsuru Honda, Motoki Nakata, Nobuhiko Hayashi, Tomonari Nakayama, Maiko Yoshida. Modification of a machine learning-based semi-empirical turbulent transport model for its versatility. Contributions to Plasma Physics. 2023
E. Narita, M. Honda, S. Maeyama, T.-H. Watanabe. Toward efficient runs of nonlinear gyrokinetic simulations assisted by a convolutional neural network model recognizing wavenumber-space images. Nuclear Fusion. 2022. 62. 8. 086037-086037
M Yoshida, G Giruzzi, N Aiba, JF Artaud, J Ayllon-Guerola, L Balbinot, O Beeke, E Belonohy, P Bettini, W Bin, et al. Plasma physics and control studies planned in JT-60SA for ITER and DEMO operations and risk mitigation. PLASMA PHYSICS AND CONTROLLED FUSION. 2022. 64. 5. 054004
Empirical transport modeling for predicting the edge region of H-mode plasmas
(Japan-Korea Workshop on Fusion Theory 2024 2024)
ITPA T&C, Joint experiments/activity proposal, Title: Reduced models of transport in the H-mode pedestal region for integrated simulation
(Transpor;Confinement Topical;Group Meeting 2024)
Convolutional neural network models for forecasting heat fluxes calculated by nonlinear gyrokinetic simulations
(US-Japan Joint Institute Fusion Theory (JIFT) Collaboration Meeting on Exascale Computing 2023)
