Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning
Existing operator learning methods for irregular geometric domains suffer from high memory consumption and poor geometric adaptability. Method: This paper proposes the Deep Integral Operator (DIO) framework, which employs learnable compactly supported kernel functions and a sparsity-aware parametrization scheme to jointly ensure smoothness and computational efficiency; it further incorporates adaptive numerical quadrature to achieve geometry-agnostic modeling, eliminating reliance on structured grids. Contribution/Results: On standard benchmarks, DIO achieves higher accuracy than state-of-the-art neural operators, with improved training and test accuracy. It reduces trainable parameters by over an order of magnitude, significantly enhancing memory efficiency, generalization capability, and geometric robustness.