Knowledge-Constrained Shape Optimization with a Mixture-of-Experts Neural Operator for High-Confidence Design
A Mixture-of-Experts Neural Operator (MoE-NO) is developed to improve drag prediction and trend consistency over heterogeneous aerodynamic datasets and an uncertainty-estimation strategy is introduced to detect out-of-distribution geometries and selectively trigger physics-solver feedback for local sample enrichment.