The authors build "digital twins" (real-time surrogate models) for thermal energy distribution systems by combining high-fidelity Modelica simulations with simpler surrogates (including physics-informed models like SINDyC and neural networks like GRUs) trained via active learning. Active learning intelligently selects which simulation trajectories to query, cutting the data requirement by up to 5× compared to random sampling while maintaining accuracy and enabling uncertainty quantification for real-time control.
Main takeaways:
- High-fidelity thermal system simulations are too slow for real-time control; purely data-driven surrogates can be fragile.
- The authors couple system-level Modelica simulations with four surrogate types: SINDyC (sparse dynamics identification), probabilistic MvG-SINDyC, feedforward neural nets, and GRU recurrent nets.
- Active learning query strategies tailored to each surrogate (e.g., Mahalanobis distance for MvG-SINDyC, prediction error for others) prioritize informative trajectories.
- On a glycol heat exchanger subsystem, active learning achieves comparable accuracy with one-fifth the simulation cost; GRU is most accurate, SINDyC most efficient and interpretable, and MvG-SINDyC enables uncertainty quantification.