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- W4323643046 abstract "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．" @default.
- W4323643046 created "2023-03-10" @default.
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- W4323643046 date "2023-01-01" @default.
- W4323643046 modified "2023-10-05" @default.
- W4323643046 title "EFFICIENCY IMPROVEMENT OF PINNS INVERSE ANALYSIS BY EXTRACTING SPATIAL FEATURES OF DATA" @default.
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- W4323643046 doi "https://doi.org/10.2208/jscejj.22-15011" @default.
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