1 code implementation • 15 Dec 2023 • Ximeng Ye, Hongyu Li, Jingjie Huang, Guoliang Qin
This paper launches a thorough discussion on the locality of local neural operator (LNO), which is the core that enables LNO great flexibility on varied computational domains in solving transient partial differential equations (PDEs).
1 code implementation • 11 Mar 2022 • Hongyu Li, Ximeng Ye, Peng Jiang, Guoliang Qin, Tiejun Wang
For demonstration, LNO learns Navier-Stokes equations from randomly generated data samples, and then the pre-trained LNO is used as an explicit numerical time-marching scheme to solve the flow of fluid on unseen domains, e. g., the flow in a lid-driven cavity and the flow across the cascade of airfoils.
