{"@context":{"@vocab":"https://cir.nii.ac.jp/schema/1.0/","rdfs":"http://www.w3.org/2000/01/rdf-schema#","dc":"http://purl.org/dc/elements/1.1/","dcterms":"http://purl.org/dc/terms/","foaf":"http://xmlns.com/foaf/0.1/","prism":"http://prismstandard.org/namespaces/basic/2.0/","cinii":"http://ci.nii.ac.jp/ns/1.0/","datacite":"https://schema.datacite.org/meta/kernel-4/","ndl":"http://ndl.go.jp/dcndl/terms/","jpcoar":"https://github.com/JPCOAR/schema/blob/master/2.0/"},"@id":"https://cir.nii.ac.jp/crid/1390013884655543552.json","@type":"Article","productIdentifier":[{"identifier":{"@type":"DOI","@value":"10.2208/jscejj.22-15011"}},{"identifier":{"@type":"URI","@value":"https://www.jstage.jst.go.jp/article/jscejj/79/15/79_22-15011/_pdf"}}],"resourceType":"学術雑誌論文(journal article)","dc:title":[{"@language":"en","@value":"EFFICIENCY IMPROVEMENT OF PINNS INVERSE ANALYSIS BY EXTRACTING SPATIAL FEATURES OF DATA"},{"@language":"ja","@value":"空間特徴量抽出を援用した PINNs によるパラメータ逆解析の効率化"}],"dc:language":"ja","description":[{"type":"abstract","notation":[{"@language":"en","@value":"<p>With the rapid increase in torrential rainfall disasters and associated landslides, the demand for predictive numerical simulations has been growing. Due to computational limits, one needs to introduce approximations, however, the parameters to link detailed and approximated simulations (e.g. drag / bed friction coefficients) are determined empirically, and their applicability remains vague. In this context, this paper presents the application of a deep learning model, PINN (Physics-Informed Neural Network) to inverse analysis. This work assumes a scenario where one has an access to limited data (which is the case for real-site observation), and proposes utilizing data’s spatial features extracted from POD (Proper Orthogonal Decomposition) instead of conventional random number-based method. We found that proposed method supports PINN for faster training convergence and efficient parameter identification.</p>"},{"@language":"ja","@value":"<p>頻発化・激甚化する豪雨災害およびそれに伴う土砂災害に対して，数値シミュレーションによる予測が求められている．大規模なシミュレーションを実施する際，計算資源の制約から幾つかの近似操作が導入されるが，近似解析と詳細解析を紐付けるパラメータ（抗力係数，粗度係数など）は経験的に定められており，適用範囲が不明瞭である．本論文では，深層学習モデルPINN（Physics-Informed Neural Network）を用いて，観測値からパラメータを逆解析する問題に取り組む．実観測で予想される，限定的なデータのみが取得可能な条件において，固有直交分解（POD: Proper Orthogonal Decomposition）に基づき空間特徴量が極大・極小となる領域から重点的にデータを取得することで，従来の乱数を用いたデータの取得と比較して効率的な学習とパラメータ逆解析が可能であることを確認した．</p>"}],"abstractLicenseFlag":"disallow"}],"creator":[{"@id":"https://cir.nii.ac.jp/crid/1410013884655543554","@type":"Researcher","foaf:name":[{"@language":"en","@value":"DEGUCHI Shota"},{"@language":"ja","@value":"出口 翔大"}],"jpcoar:affiliationName":[{"@language":"ja","@value":"九州大学大学院　工学府土木工学専攻"}]},{"@id":"https://cir.nii.ac.jp/crid/1410013884655543552","@type":"Researcher","foaf:name":[{"@language":"en","@value":"SHIBATA Yosuke"},{"@language":"ja","@value":"柴田 洋佑"}],"jpcoar:affiliationName":[{"@language":"ja","@value":"九州大学大学院　工学府土木工学専攻"}]},{"@id":"https://cir.nii.ac.jp/crid/1420001326215030912","@type":"Researcher","personIdentifier":[{"@type":"KAKEN_RESEARCHERS","@value":"90411230"},{"@type":"NRID","@value":"1000090411230"},{"@type":"CINII_AUTHOR_ID","@value":"DA16389029"},{"@type":"URI","@value":"https://ci.nii.ac.jp/author/DA16389029#entity"},{"@type":"URI","@value":"https://viaf.org/viaf/NII%7CDA16389029"},{"@type":"NRID","@value":"9000303988104"},{"@type":"NRID","@value":"9000402618896"},{"@type":"NRID","@value":"9000408583535"},{"@type":"NRID","@value":"9000408771554"},{"@type":"NRID","@value":"9000241452053"},{"@type":"NRID","@value":"9000241460500"},{"@type":"NRID","@value":"9000241578987"},{"@type":"NRID","@value":"9000241567586"},{"@type":"NRID","@value":"9000329415455"},{"@type":"NRID","@value":"9000386211921"},{"@type":"NRID","@value":"9000413902295"},{"@type":"NRID","@value":"9000396131422"},{"@type":"NRID","@value":"9000255808877"},{"@type":"NRID","@value":"9000241195607"},{"@type":"NRID","@value":"9000241454130"},{"@type":"NRID","@value":"9000326252516"},{"@type":"NRID","@value":"9000356893525"},{"@type":"NRID","@value":"9000304311305"},{"@type":"NRID","@value":"9000398819428"},{"@type":"NRID","@value":"9000392252445"},{"@type":"NRID","@value":"9000255808738"},{"@type":"NRID","@value":"9000256363012"},{"@type":"NRID","@value":"9000241569468"},{"@type":"NRID","@value":"9000409515214"},{"@type":"NRID","@value":"9000283784990"},{"@type":"NRID","@value":"9000415088764"},{"@type":"NRID","@value":"9000257780019"},{"@type":"NRID","@value":"9000241439895"},{"@type":"NRID","@value":"9000241441376"},{"@type":"NRID","@value":"9000313609862"},{"@type":"NRID","@value":"9000386211401"},{"@type":"NRID","@value":"9000415088907"},{"@type":"NRID","@value":"9000413902544"},{"@type":"NRID","@value":"9000413902341"},{"@type":"NRID","@value":"9000283600226"},{"@type":"NRID","@value":"9000283357913"},{"@type":"NRID","@value":"9000257797111"},{"@type":"NRID","@value":"9000356893535"},{"@type":"NRID","@value":"9000329415486"},{"@type":"NRID","@value":"9000251869516"},{"@type":"NRID","@value":"9000304310852"},{"@type":"NRID","@value":"9000303987413"},{"@type":"NRID","@value":"9000283784729"},{"@type":"NRID","@value":"9000408583462"},{"@type":"NRID","@value":"9000256362388"},{"@type":"NRID","@value":"9000241448593"},{"@type":"NRID","@value":"9000241202703"},{"@type":"NRID","@value":"9000409518240"},{"@type":"NRID","@value":"9000394964807"},{"@type":"NRID","@value":"9000394960274"},{"@type":"NRID","@value":"9000241623738"},{"@type":"NRID","@value":"9000254807621"},{"@type":"NRID","@value":"9000255808575"},{"@type":"NRID","@value":"9000241442270"},{"@type":"NRID","@value":"9000021346302"},{"@type":"NRID","@value":"9000402618555"},{"@type":"NRID","@value":"9000412377633"},{"@type":"NRID","@value":"9000412373974"},{"@type":"NRID","@value":"9000296677810"},{"@type":"NRID","@value":"9000251862527"},{"@type":"NRID","@value":"9000326252489"},{"@type":"NRID","@value":"9000415158516"},{"@type":"NRID","@value":"9000329415522"},{"@type":"NRID","@value":"9000382214807"},{"@type":"NRID","@value":"9000241205731"},{"@type":"NRID","@value":"9000403026351"},{"@type":"NRID","@value":"9000021701545"},{"@type":"NRID","@value":"9000283784972"},{"@type":"NRID","@value":"9000020821676"},{"@type":"NRID","@value":"9000415088801"},{"@type":"NRID","@value":"9000392252455"},{"@type":"NRID","@value":"9000018680056"},{"@type":"NRID","@value":"9000409515366"},{"@type":"NRID","@value":"9000296676212"},{"@type":"NRID","@value":"9000251866226"},{"@type":"NRID","@value":"9000394964821"},{"@type":"NRID","@value":"9000394957232"},{"@type":"NRID","@value":"9000412651120"},{"@type":"NRID","@value":"9000283617902"},{"@type":"NRID","@value":"9000241201683"},{"@type":"NRID","@value":"9000413902075"},{"@type":"NRID","@value":"9000402618234"},{"@type":"NRID","@value":"9000392252448"},{"@type":"NRID","@value":"9000241190741"},{"@type":"NRID","@value":"9000241457603"},{"@type":"NRID","@value":"9000409518264"},{"@type":"NRID","@value":"9000251866201"},{"@type":"NRID","@value":"9000356894673"},{"@type":"NRID","@value":"9000356895419"},{"@type":"NRID","@value":"9000411391867"},{"@type":"NRID","@value":"9000411391862"},{"@type":"NRID","@value":"9000392246491"},{"@type":"NRID","@value":"9000258444492"},{"@type":"NRID","@value":"9000329415410"},{"@type":"NRID","@value":"9000414228008"},{"@type":"NRID","@value":"9000399512558"},{"@type":"NRID","@value":"9000296676487"},{"@type":"NRID","@value":"9000326254699"},{"@type":"NRID","@value":"9000403026374"},{"@type":"NRID","@value":"9000402618164"},{"@type":"NRID","@value":"9000387478001"},{"@type":"NRID","@value":"9000241578754"},{"@type":"NRID","@value":"9000251862801"},{"@type":"NRID","@value":"9000403026779"},{"@type":"NRID","@value":"9000394961496"},{"@type":"NRID","@value":"9000386211890"},{"@type":"NRID","@value":"9000408584004"},{"@type":"NRID","@value":"9000408583677"},{"@t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Mitsuteru"},{"@language":"ja","@value":"浅井 光輝"}],"jpcoar:affiliationName":[{"@language":"ja","@value":"九州大学大学院　工学研究院社会基盤部門"}]}],"publication":{"publicationIdentifier":[{"@type":"EISSN","@value":"24366021"}],"prism:publicationName":[{"@language":"en","@value":"Japanese Journal of JSCE"},{"@language":"ja","@value":"土木学会論文集"},{"@language":"en","@value":"Japanese Journal of JSCE"},{"@language":"ja","@value":"土木学会論文集"}],"dc:publisher":[{"@language":"en","@value":"Japan Society of Civil Engineers"},{"@language":"ja","@value":"公益社団法人 土木学会"}],"prism:publicationDate":"2023","prism:volume":"79","prism:number":"15","prism:startingPage":"n/a"},"reviewed":"false","url":[{"@id":"https://www.jstage.jst.go.jp/article/jscejj/79/15/79_22-15011/_pdf"}],"availableAt":"2023","foaf:topic":[{"@id":"https://cir.nii.ac.jp/all?q=deep%20learning","dc:title":"deep learning"},{"@id":"https://cir.nii.ac.jp/all?q=data-driven%20science","dc:title":"data-driven science"},{"@id":"https://cir.nii.ac.jp/all?q=inverse%20analysis","dc:title":"inverse analysis"},{"@id":"https://cir.nii.ac.jp/all?q=physics-informed%20neural%20networks","dc:title":"physics-informed neural networks"},{"@id":"https://cir.nii.ac.jp/all?q=proper%20orthogonal%20decomposition","dc:title":"proper orthogonal decomposition"},{"@id":"https://cir.nii.ac.jp/all?q=deep%20learning","dc:title":"deep learning"},{"@id":"https://cir.nii.ac.jp/all?q=data-driven%20science","dc:title":"data-driven science"},{"@id":"https://cir.nii.ac.jp/all?q=inverse%20analysis","dc:title":"inverse analysis"},{"@id":"https://cir.nii.ac.jp/all?q=physics-informed%20neural%20networks","dc:title":"physics-informed neural networks"},{"@id":"https://cir.nii.ac.jp/all?q=proper%20orthogonal%20decomposition","dc:title":"proper orthogonal 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