{"created":"2023-05-15T14:57:17.712986+00:00","id":77752,"links":{},"metadata":{"_buckets":{"deposit":"c3868b8a-d241-41af-b61f-1f4a5b738de9"},"_deposit":{"created_by":1,"id":"77752","owners":[1],"pid":{"revision_id":0,"type":"depid","value":"77752"},"status":"published"},"_oai":{"id":"oai:repo.qst.go.jp:00077752","sets":["10:29"]},"author_link":["808264","808263","808265","808266"],"item_10005_date_7":{"attribute_name":"発表年月日","attribute_value_mlt":[{"subitem_date_issued_datetime":"2019-11-30","subitem_date_issued_type":"Issued"}]},"item_10005_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"硬い輸送モデルを用いた安定した分布予測のため、大域的最適化手法を用いた新しい定常輸送コードGOTRESSが開発された。数値的不安定なしに温度分布が得られた一方、多数の輸送モデル評価を必要とし、長い計算時間を要した。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開発により、大域的最適化手法の弱点を克服して高速な計算を実現した。更に、機械学習法による最適化により、モデルの精度を大きく向上させた。","subitem_description_type":"Abstract"}]},"item_10005_description_6":{"attribute_name":"会議概要（会議名, 開催地, 会期, 主催者等）","attribute_value_mlt":[{"subitem_description":"第36回プラズマ・核融合学会　年会","subitem_description_type":"Other"}]},"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":"808263","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"成田, 絵美"}],"nameIdentifiers":[{"nameIdentifier":"808264","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Honda, Mitsuru","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"808265","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Narita, Emi","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"808266","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":"conference object","resourceuri":"http://purl.org/coar/resource_type/c_c94f"}]},"item_title":"機械学習法を用いたGOTRESSシミュレーションの高速化","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"機械学習法を用いたGOTRESSシミュレーションの高速化"}]},"item_type_id":"10005","owner":"1","path":["29"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-12-02"},"publish_date":"2019-12-02","publish_status":"0","recid":"77752","relation_version_is_last":true,"title":["機械学習法を用いたGOTRESSシミュレーションの高速化"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2023-05-15T23:37:31.510684+00:00"}