@inproceedings{oai:repo.qst.go.jp:00085504,
 author = {Emi, Narita and Mitsuru, Honda and Motoki, Nakata and Maiko, Yoshida and Nobuhiko, Hayashi and Emi, Narita and Maiko, Yoshida and Nobuhiko, Hayashi},
 book = {第19回核燃焼プラズマ統合コード研究会},
 month = {Feb},
 note = {The gyrokinetic-based turbulent transport models are essential to predict density and temperature profiles, but introducing detailed descriptions of turbulence physics tends to increase the computational cost. To accelerate the profile predictions, a neural-network (NN) based approach has been undertaken. Our study is also developing a NN-based turbulent transport model DeKANIS. A turbulent saturation rule employed in DeKANIS was based on experimental particle fluxes estimated for JT-60U H-mode plasmas, and it was apt to overestimate temperatures. To reduce the overestimation, a different saturation rule is built including experimental heat fluxes.},
 title = {Development of a semi-empirical particle and heat transport model and improvement in its turbulent saturation rule},
 year = {2022}
}