{"created":"2023-05-15T14:56:41.021250+00:00","id":76925,"links":{},"metadata":{"_buckets":{"deposit":"c3c0c505-a386-42ff-bfd3-1fdd8fe11adf"},"_deposit":{"created_by":1,"id":"76925","owners":[1],"pid":{"revision_id":0,"type":"depid","value":"76925"},"status":"published"},"_oai":{"id":"oai:repo.qst.go.jp:00076925","sets":["11"]},"author_link":["786187","786186"],"item_10004_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2019-09","bibliographicIssueDateType":"Issued"},"bibliographic_titles":[{"bibliographic_title":"計算工学ナビ・ニュースレター2019年秋号"}]}]},"item_10004_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"磁場閉じ込めプラズマの研究において、プラズマ温度分布を正確かつ高速に予測することは極めて重要である。高度な乱流輸送モデルは数値的安定に解くのが困難である上に、計算にも時間を要する。大域的最適化手法を用いた新しい輸送コードの開発により数値的安定に解くことが可能となった。輸送モデルを模擬するニューラルネットワークモデルの構築により、高速かつ正確に輸送モデルが出力する輸送流束を再現する事が可能になり、プラズマ温度分布予測の大幅な高速化に繋がった。","subitem_description_type":"Abstract"}]},"item_10004_publisher_8":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"東京大学生産技術研究所　革新的シミュレーション研究センター"}]},"item_access_right":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"metadata only access","subitem_access_right_uri":"http://purl.org/coar/access_right/c_14cb"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"本多, 充"}],"nameIdentifiers":[{"nameIdentifier":"786186","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Honda, Mitsuru","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"786187","nameIdentifierScheme":"WEKO"}]}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"磁場閉じ込め核融合プラズマの温度予測を機械学習で加速する","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"磁場閉じ込め核融合プラズマの温度予測を機械学習で加速する"}]},"item_type_id":"10004","owner":"1","path":["11"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-09-25"},"publish_date":"2019-09-25","publish_status":"0","recid":"76925","relation_version_is_last":true,"title":["磁場閉じ込め核融合プラズマの温度予測を機械学習で加速する"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2023-05-16T00:09:37.795299+00:00"}