A Greedy PDE Router for Blending Neural Operators and Classical Methods

ArXi:2509.24814v2 Announce Type: replace-cross When solving PDEs, classical numerical solvers are often computationally expensive, while machine learning methods can suffer from spectral bias, failing to capture high-frequency components. Designing an optimal hybrid iterative solver--where, at each iteration, a solver is selected from an ensemble of solvers to leverage their complementary strengths--poses a challenging combinatorial problem.