Fourier Neural Operator

Fourier Neural Operators (FNOs) are a class of deep learning models designed to efficiently learn and approximate operators mapping between function spaces, particularly those arising from partial differential equations (PDEs). Current research focuses on improving FNO architectures, such as incorporating physics-informed constraints, developing parallel and multi-resolution structures, and exploring their application to diverse scientific problems including fluid dynamics, quantum systems, and material science. This approach offers significant potential for accelerating complex simulations and improving the accuracy of predictions in various scientific and engineering domains by providing fast, data-driven surrogates for computationally expensive traditional methods.