Learning Stiff Dynamical Operators: Scaling, Fast-Slow Excitation, and Eigen-Consistent Neural Models
This paper introduces a neural operator learning framework that overcomes the challenges of stiff dynamical systems by employing stiffness-aware scaling, fast-direction excitation, and eigen-structure diagnostics to achieve spectral fidelity and accurate multi-scale modeling.