Principled approaches for extending neural architectures to function spaces for operator learning

Nature Machine Intelligence, Published online: 03 July 2026; doi:10.1038/s42256-026-01267-z Berner et al. show how to adapt popular neural networks into discretization-agnostic neural operators that learn from continuous scientific data, enabling scientific simulations that generalize more reliably across resolutions.