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Machine-learning assisted steady-state profile predictions using global optimization techniques
https://repo.qst.go.jp/records/77189
https://repo.qst.go.jp/records/77189f605550c-c403-4f7d-8b05-22fe0eaff82a
| Item type | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2019-10-23 | |||||
| タイトル | ||||||
| タイトル | Machine-learning assisted steady-state profile predictions using global optimization techniques | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
| 資源タイプ | journal article | |||||
| アクセス権 | ||||||
| アクセス権 | metadata only access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_14cb | |||||
| 著者 |
Honda, Mitsuru
× Honda, Mitsuru× Narita, Emi× Mitsuru, Honda× Emi, Narita |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Predicting plasma profiles with a stiff turbulent transport model is important for experimental analysis and development of operation scenarios. Due to the sensitivity of turbulent fluxes to profile gradients, robust predictions are still arduous with a stiff model incorporated in a conventional transport code. With global optimization techniques employed, the new steady-state transport code, global optimization version of the transport equation stable solver, has been developed to overcome these difficulties. It enables us to attain smooth profiles of diffusivity and temperature even though jagged profiles thereof are inclined to emerge in simulations with a stiff model. A neural-network-based surrogate model of a transport model is developed to compensate slow computation inherent to global optimization. Hyperparameter optimization realizes the surrogate model with very good accuracy. | |||||
| 書誌情報 |
Physics of Plasmas 巻 26, 号 10, p. 102307-1-102307-15, 発行日 2019-10 |
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| 出版者 | ||||||
| 出版者 | The American Institute of Physics | |||||
| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 1070-664X | |||||
| DOI | ||||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | 10.1063/1.5117846 | |||||
| 関連サイト | ||||||
| 識別子タイプ | URI | |||||
| 関連識別子 | https://aip.scitation.org/doi/full/10.1063/1.5117846 | |||||
