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This paper introduces the Iterative Refinement Neural Operator (IRNO), which augments pretrained neural operators with a learned refinement module applied via fixed-point iteration to mitigate spectral bias. IRNO progressively corrects high-frequency errors, achieving up to 56% improvement on turbulent flow and showing stable extrapolation beyond the trained iteration count.
Tohoku University researchers have overturned an 80-year aeronautical engineering principle by demonstrating that distributed micro-roughness (DMR) can reduce aerodynamic drag by up to 43.6%, a finding with significant implications for high-speed vehicles.