Zongyi Li

This blog takes about 10 minutes to read. It introduces the Fourier neural operator that solves a family of PDEs from scratch. It the first work that can learn resolution-invariant solution operators on Navier-Stokes equation, achieving state-of-the-art accuracy among all existing deep learning methods and up to 1000x faster than traditional solvers. Thinking in continuum gives us an advantage when dealing with PDE. We want to design mesh-indepedent, resolution-invariant operators.