AI RESEARCH
Scalable Physics-Informed Neural Differential Equations and Data-Driven Algorithms for HVAC Systems
arXiv CS.LG
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ArXi:2604.18438v1 Announce Type: new We present a scalable, data-driven simulation framework for large-scale heating, ventilation, and air conditioning (HVAC) systems that couples physics-informed neural ordinary differential equations (PINODEs) with differential-algebraic equation (DAE) solvers. At the component level, we learn heat-exchanger dynamics using an implicit PINODE formulation that predicts conserved quantities (refrigerant mass $M_r$ and internal energy $E_\text{hx}$) as outputs, enabling physics-informed.