CAN-PINN: a fast physics-informed neural network based on coupled-automatic-numerical differentiation method

CAN-PINN: a fast physics-informed neural network based on coupled-automatic-numerical differentiation method

In this study, novel physics-informed neural network (PINN) methods for coupling neighboring support points and their derivative terms which are obtained by automatic differentiation (AD), are proposed to allow efficient training with improved accuracy. The computation of differential operators requ...

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書目詳細資料
Main Authors:	Chiu, Pao-Hsiung, Wong, Jian Cheng, Ooi, Chinchun, Dao, My Ha, Ong, Yew-Soon
其他作者:	School of Computer Science and Engineering
格式:	Article
語言:	English
出版:	2022
主題:	Engineering::Computer science and engineering Physics-Informed Neural Network Training Loss Formulation
在線閱讀:	https://hdl.handle.net/10356/162602
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!

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