Smooth Piecewise Cutting for Neural Operator to Handle Discontinuities and Sharp Transitions

ArXi:2605.19823v1 Announce Type: cross Neural operators have achieved strong performance in learning solution operators of partial differential equations (PDEs), but their inherently continuous representations struggle to capture discontinuities and sharp transitions. Existing approaches typically approximate such features within continuous function spaces, often requiring increased model capacity and high-resolution data. In this work, we propose Cut-DeepONet, a two-stage