Physics-informed neural networks for low Reynolds number flows over cylinder

• Main Authors: Ang, Elijah Hao Wei, Wang, Guangjian, Ng, Bing Feng
• 其他作者: School of Mechanical and Aerospace Engineering
• 格式: Article
• 語言: English
• 出版: 2023
• 主題: Engineering::Mechanical engineering; Physics-Informed Neural Network; Low Reynolds Number
• 在線閱讀: https://hdl.handle.net/10356/171076
• 機構: Nanyang Technological University

Physics-informed neural network (PINN) architectures are recent developments that can act as surrogate models for fluid dynamics in order to reduce computational costs. PINNs make use of deep neural networks, where the Navier-Stokes equation and freestream boundary conditions are used as losses of t...