文献
J-GLOBAL ID:202202281015810366
整理番号:22A0942281
ニューラルネットワークシフト固有直交分解:双曲型方程式の非線形縮小のための機械学習アプローチ【JST・京大機械翻訳】
The Neural Network shifted-proper orthogonal decomposition: A machine learning approach for non-linear reduction of hyperbolic equations
著者 (6件):
Papapicco Davide
(Mathematics Area, mathLab, SISSA, Via Bonomea, 265, Trieste, 34136, Italy)
,
Papapicco Davide
(Department of Electronics and Telecommunications, Politecnico di Torino, C.so Duca degli Abruzzi, 24, Torino, 10129, Italy)
,
Demo Nicola
(Mathematics Area, mathLab, SISSA, Via Bonomea, 265, Trieste, 34136, Italy)
,
Girfoglio Michele
(Mathematics Area, mathLab, SISSA, Via Bonomea, 265, Trieste, 34136, Italy)
,
Stabile Giovanni
(Mathematics Area, mathLab, SISSA, Via Bonomea, 265, Trieste, 34136, Italy)
,
Rozza Gianluigi
(Mathematics Area, mathLab, SISSA, Via Bonomea, 265, Trieste, 34136, Italy)
資料名:
Computer Methods in Applied Mechanics and Engineering
(Computer Methods in Applied Mechanics and Engineering)
巻:
392
ページ:
Null
発行年:
2022年
JST資料番号:
E0856A
ISSN:
0045-7825
資料種別:
逐次刊行物 (A)
記事区分:
原著論文
発行国:
オランダ (NLD)
言語:
英語 (EN)