Deep Operator

Deep operator networks (DeepONets) are a class of neural networks designed to learn mappings between infinite-dimensional function spaces, primarily focusing on approximating complex operators, such as those defined by partial differential equations (PDEs). Current research emphasizes improving DeepONet efficiency and robustness through techniques like randomized sampling, multigrid methods, and the development of resolution-independent architectures. This approach holds significant promise for accelerating scientific simulations across diverse fields, from fluid dynamics and climate modeling to material science and power grid analysis, by providing accurate and computationally efficient surrogate models for complex physical systems.