TENSORnet: A Physics-Informed Entropy Protocol for Infrastructure-Induced Metabolic Arrest Detection in Cross-Asset Financial Networks

Physical infrastructure failures expose a structural blind spot in conventional financial risk systems: when the electricity supply becomes binding, markets enter metabolic arrest, a state where information-processing capacity collapses, and standard correlation-based metrics misread rising cross-asset correlations as stability. This paper introduces TENSORnet (Temporal Entropy-aware Network for Systemic Onset Recognition), a physics-informed computational protocol that fuses a VIX-calibrated inhibitory gate γL(t) (NLS-fitted, R2 = 0.30) with cross-asset Shannon entropy to detect infrastructure-induced stress before it materialises in price-based indicators. Applied to a temporal cross-asset graph of 2,838 JSE trading days (05 January 2015 to 29 April 2026) across seven asset classes under South Africa’s load-shedding crisis, TENSORnet achieves Precision = 100.0%, Recall = 85.8%, F1 = 92.4%, AUC = 1.000, and zero false positive alarms (Stage 3+ definition) on 1,830 out- of-sample days, with a mean lead time of 17 calendar days (408 hours) before fragile regime onset, outperforming all benchmarks, including XGBoost (F1 = 71.3%). Ablation confirms the physics-informed gate as the dominant architectural component (ΔF1 = −92.4 pp on removal): statistical learning alone recovers nothing (AUC = 0.469). The Densification Paradox, rising cross-asset correlation with falling entropy under stress (r = −0.468, p < 0.001), is confirmed empirically for the first time in cross-asset data. A Thermodynamic Port-Hamiltonian Neural ODE (TPH-NODE) extension grounds metabolic arrest in the second law of thermodynamics.