PIANO: A new operator learning framework that deciphers and incorporates invariants from the PDE series

In a recent article published in the National Science Review, researchers have proposed a new operator learning framework called PIANO. PIANO uses self-supervised learning to extract representations containing physical invariants from partial differential equations (PDEs) systems with different physical mechanisms, thereby extending the generalization ability of neural operators to various physics scenarios.

article computer sciences differential extract framework researchers review science self-supervised learning series supervised learning systems