DeepONet Architecture

DeepONets (Deep Operator Networks) are a type of neural network designed to efficiently learn mappings between functions, enabling rapid solutions to complex partial differential equations (PDEs) that model various physical systems. Current research focuses on improving DeepONet training efficiency through techniques like randomized sampling and incorporating physics-informed constraints, as well as enhancing their flexibility and interpretability using methods like singular value decomposition. This approach offers significant speedups over traditional numerical methods for solving PDEs, impacting fields like climate modeling, porous media transport, and nanoscale heat transfer by enabling faster and more accurate simulations.