The Kuramoto Neural Operator: Learning to Solve PDEs via Coupled Oscillator Dynamics

This paper introduces the Kuramoto Neural Operator for learning solution operators of partial differential equations. The approach explores an alternative perspective motivated by coupled oscillator dynamics, addressing the limitation of existing architectures that represent solution operators in fixed bases. This framing is particularly suited for phenomena governed by local interactions in physical space, where global basis representations may be less effective.