Deep Operator Network

Deep Operator Networks (DeepONets) are a class of neural networks designed to learn mappings between infinite-dimensional function spaces, primarily focusing on efficiently solving partial differential equations (PDEs). Current research emphasizes improving DeepONet architectures, such as incorporating Fourier transforms, hypernetworks, and parallel processing, to enhance accuracy, efficiency, and generalization across diverse PDEs and datasets, including those with limited data or high dimensionality. This approach holds significant promise for accelerating scientific computation and engineering design by providing fast, accurate surrogate models for complex physical systems, impacting fields ranging from fluid dynamics to materials science.