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

• Main Authors: Ang, Elijah Hao Wei, Wang, Guangjian, Ng, Bing Feng
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Mechanical engineering; Physics-Informed Neural Network; Low Reynolds Number
• Online Access: https://hdl.handle.net/10356/171076
• Institution: 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...