Material-Resolved Thermal Boundary Constraints for PINN-Based Urban Micro-Meteorology

Urban micro-meteorological models often omit building surfaces’ evening heat release. We derive physics-informed thermal boundary constraints from the Surface Energy Balance equation, supplied to a Physics-Informed Neural Network (PINN) through an Urban Thermal-Geometric Database and a Force-Restore model. A 2D heated cavity benchmark at Ra=104, verified against an independent finite-volume reference, reduces interior temperature MAE by 56%, wall-temperature MAE by 97.5% and Nusselt error by 74% across five seeds, at a 16% horizontal-velocity cost, worse in all five. In the open-roof canyon, a five-seed PINN feasibility comparison shows material-resolved wall temperatures reduce blind temperature MAE from 0.00830±0.00050 to 0.00368±0.00058, with much lower matched heat-flux errors than an equivalent-memory homogeneous control and velocity within seed scatter. Seven independent quasi-steady snapshots follow. At 14:00, material resolution raises street-surface heat transfer by 61% while corridor-band velocity variability remains comparable, indicating redistribution rather than amplification. At 18:00 the material-resolved case retains an order-of-magnitude larger σv (0.61 vs. 0.06); at 20:00 it reverses the street-surface heat-flux direction while the mean-temperature case approaches neutrality. It is a constructed near-zero-forcing baseline; ratios to it are not interpreted. The reversal is robust in sign but not in magnitude under slab-model and ±1 K facade-contrast perturbations.