Neural Operator: Is data all you need to model the world? An insight into the paradigm of data-driven scientific ML

ArXi:2301.13331v3 Announce Type: replace-cross Numerical approximations of partial differential equations (PDEs) are routinely employed to formulate the solution of physics, engineering, and mathematical problems involving functions of several variables, such as the propagation of heat or sound, fluid flow, elasticity, electrostatics, electrodynamics, and more. While this has led to solving many complex phenomena, there are some limitations.