Trust-Calibrated Certified Repair for Physics-Constrained Decisions under Localized Model Misspecification

Abstract

Feasibility-restoration layers turn learned, market-based, or optimizer-generated decisions into actions satisfying hard constraints in systems such as power grids. Yet a repair is only as trustworthy as its constraint model: line parameters, sensitivities, ratings, and topology can be locally wrong, so a decision certified feasible under the nominal model may violate the deployed system. We identify this false safety as a dominant failure mode of model-trusting repair and propose Trust-Calibrated Certified Repair (TCR). TCR treats repair as trust calibration and answers four questions in one pipeline: where the physical model is wrong, discovered from measurements with false-discovery control; how much each constraint should be trusted, set by test-gated shrinkage and uncertainty-proportional security margins; what least-cost intervention restores feasibility, computed by a certified repair program; and why the cost was paid, attributed to genuine congestion versus avoidable model error through dual prices. On a physically grounded dynamic-line-rating benchmark whose true ratings follow IEEE 738 under real weather, TCR reaches 98% true-network feasibility, within two points of a clairvoyant oracle, at lower-than-naive cost and with perfect localization. Model-trusting repair, robust margins, and chance-constrained tightening leave substantial feasibility or cost gaps. The same method transfers unchanged to transmission redispatch over PGLib-OPF networks and distribution voltage regulation on the IEEE 33-bus feeder. Across all three task families, TCR gives the strongest deployable feasibility-cost frontier under localized physical-model misspecification. Calibrating trust in the constraint model is the missing ingredient for reliable AI-assisted engineering decisions.