Iterative Refinement Neural Operators are Learned Fixed-Point Solvers: A Principled Approach to Spectral Bias Mitigation

This paper introduces the Iterative Refinement Neural Operator (IRNO) to mitigate spectral bias in neural operators, using a learned refinement module via fixed-point iteration. IRNO decomposes predictions into a coarse initialization followed by successive residual corrections, achieving significant error reduction across physical systems.