A Greedy PDE Router for Blending Neural Operators and Classical Methods
The paper proposes an approximate greedy router to select between classical numerical solvers and neural operators at each step of solving Partial Differential Equations (PDEs). This method mimics the optimal greedy strategy by choosing the solver that provides the largest immediate error reduction. The learned router achieves comparable accuracy to existing hybrid methods while requiring significantly fewer iterations.