FrFNO: Injecting the analytic Mittag-Leffler propagator into a resolution-robust neural operator for space-time fractional PDEs

Fractional partial differential equations couple a memory-dependent time derivative with a nonlocal fractional Laplacian, making repeated solution under varying parameters computationally expensive. The Fractional Fourier Neural Operator (FrFNO) addresses this by injecting the analytic Mittag-Leffler propagator directly into the neural operator architecture. This approach allows the model to capture the dominant linear fractional evolution without relearning it from data, improving both accuracy and computational efficiency for space-time fractional PDEs.