Operator learning is key to leveraging artificial intelligence for scientific discovery by enabling the solution of partial differential equations that express core principles of physical modeling.

As image datasets grow in scale and complexity, classical convolutional neural networks (CNNs) increasingly face limitations in computational efficiency, scalability, and generalisation in low-data ...