Operator Surrogate

Operator surrogates are computationally efficient approximations of complex mathematical operators, primarily focusing on solving partial differential equations (PDEs) and simulating physical processes. Current research emphasizes developing accurate and robust surrogate models using neural networks, particularly deep operator networks and variations thereof, often incorporating techniques like domain decomposition methods and multi-fidelity approaches to improve efficiency and accuracy. These surrogates offer significant advantages in accelerating simulations and solving inverse problems across diverse fields, including electromagnetics, fluid dynamics, and chemical kinetics, enabling faster design optimization and improved understanding of complex systems.