A Reality Gap Taxonomy for Physics-Informed Anomaly Detection in Industrial Control Systems: Cross-Regime Evaluation, Operating-Point and Temporal Distribution Shifts

Physics-informed anomaly detectors for industrial control systems are usually validated on a single synthetic or testbed dataset, which can yield optimistic estimates that do not transfer to deployment-like conditions. This validation-to-deployment discrepancy, the reality gap, is normally treated as one phenomenon and attributed to generic domain shift. We argue that it comprises at least three mechanistically distinct failure modes and propose a taxonomy that separates them by what a practitioner can observe and act on: Type 1, cross-regime evaluation; Type 2, operating-point shift; and Type 3, temporal distribution shift. A single physics-informed framework combining a Kalman-filter validation engine, BiLSTM, β-VAE, and graph attention network with residual sharing is evaluated across HAI 23.05, SWaT, and WADI over five random seeds. The three cases behave differently. Type 1 shows a substantial cross-regime decrease: AI-only falls from 0.774 in the prior synthetic evaluation to 0.493 on HAI, and Full EADE from 0.798 to 0.496. Under the retrospective fixed-FPR comparison on HAI, GNN+BiLSTM has the highest mean F1 at 0.498, although it is not separable from Full_EADE across five seeds. Type 2 collapses to FPR = 0.850 with F1 ≈ 0.237 across configurations; a strongly shifted sensor gives the clearest localized signature, but the failure is driven by aggregate distributional change and is recoverable by global threshold recalibration. Type 3 shows moderate FPR (0.244) and the largest sensitivity to residual occlusion (ΔF1_occl = +0.086), although a trained residual-free ablation shows no corresponding loss on any dataset. The resulting taxonomy links distinct failure signatures to different deployment responses.