Decision-Aware Residual Calibration: Bridging the Prediction–Decision Gap in Electromagnetic Design with Sparse Feedback

Sparse response feedback provides direct evidence for correcting surrogate predictions during electromagnetic design. Converting such feedback into reliable design decisions poses two coupled challenges. First, errors observed at only a few response locations must be propagated to reconstruct the complete response. Second, errors that remain after correction must be accounted for when evaluating nonlinear objectives, as they can alter candidate rankings. We propose Decision-Aware Residual Calibration (DARC), a gradient-free framework that uses sparse feedback to correct surrogate responses and account for remaining errors in nonlinear design scoring, without updating the surrogate. DARC first uses historical cross-location error dependencies to propagate observed discrepancies across the full response. DARC further identifies residual errors that may still affect design scoring after response correction. It applies the feedback-propagation operator used for the current prediction to each historical error pattern. The unrecovered component represents a possible remaining error and is added to the corrected response to form a scenario. Together, these scenarios yield a design score that accounts for how post-correction errors affect the nonlinear design objective. Across all six task–surrogate combinations, DARC achieves the lowest average response-reconstruction error and selection regret among the compared methods. In particular, DARC achieves stronger calibration performance with fewer feedback observations. Controlled ablations further show that accounting for post-correction errors improves selection under nonlinear objectives beyond scoring a single corrected response.