CG-QLEAR: Counterfactual Gain Guided Residual Correction for Vehicle Surface Pressure Modeling

Accurate vehicle surface pressure prediction requires resolving local errors while respecting applicable geometric symmetries. Yet a large prediction error or candidate residual does not necessarily indicate a beneficial correction. We introduce CG-QLEAR, a framework that selects residual updates by their predicted counterfactual gains. A candidate generator proposes local pressure updates, and a gain estimator learns the reduction in absolute error obtained by applying each update. Correction decisions and residual values are coupled across eligible mirror pairs, while asymmetric regions remain independent. A fixed point budget controls the selected residual-correction stages before final refinement. We compare structured correction with continued backbone optimization from the same 55 epoch checkpoint. The protocol uses 5,817 training, 1,148 validation, and 1,154 test vehicles from DrivAerNet++, with 32,768 surface points per vehicle. Against 15 additional backbone epochs, CG-QLEAR reduces pooled MSE by 18.48%, pooled MAE by 3.77%, and mean per vehicle maximum absolute error by 12.52%, while using 4.45% less logged optimization and validation time after the shared checkpoint, excluding one-time setup. The same correction interface also improves all five reported metrics on a Transolver backbone, supporting counterfactual gain as a useful criterion for allocating local pressure updates.