AI RESEARCH
Physics-based Digital Twins for Integrated Thermal Energy Systems Using Active Learning
arXiv CS.LG
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ArXi:2605.06756v1 Announce Type: new Real-time supervisory control of thermal energy distribution systems requires digital twins that are accurate, interpretable, and uncertainty-aware, yet remain data and computationally efficient. High-fidelity simulations alone are costly, while purely data-driven surrogates often lack robustness.