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Physics-based Digital Twins for Integrated Thermal Energy Systems Using Active Learning

topic: othertop score: 50released: 2026-05-11first surfaced: 2026-05-11arXivPDFnew_research2026-05-11

Authors: Umme Mahbuba Nabila, Paul Seurin, Linyu Lin et al.

arXiv · PDF

Summary

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.

Relevance

Not related to my work on language model personas or behavioral fine-tuning — this is a paper about physical system modeling and control. It's included here by mistake or for general ML interest, but has no connection to LLMs or the questions I'm investigating.

Abstract

arXiv:2605.06756v1 Announce Type: new Abstract: 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. To address these challenges, this work proposes an active learning (AL) framework that couples system-level Modelica simulations with four simpler physics-informed and data-driven surrogate modeling approaches: deterministic Sparse Identification of Nonlinear Dynamics with Control (SINDyC), its probabilistic multivariate-Gaussian extension (MvG-SINDyC), feedforward neural network (FNN), and gated recurrent unit (GRU) network. Tailored to each surrogate, model-specific AL query strategies are employed, including Mahalanobis-distance sampling in coefficient space for MvG-SINDyC and error-based sampling in prediction space for SINDyC, FNN, and GRU, allowing the learning process to prioritize dynamically informative trajectories. The proposed approach is demonstrated on the glycol heat exchanger (GHX) subsystem of the Thermal Energy Distribution System (TEDS) at Idaho National Laboratory. Across key GHX outputs--the bypass mass flow rate $\dot{m}{\mathrm{GHX}}$ and heat transfer rate $Q{\mathrm{GHX}}$-the AL framework achieves comparable predictive accuracy using as few as one-fifth of the simulation trajectories required by random sampling. Among the evaluated surrogates, the GRU achieves the highest predictive fidelity, while SINDyC remains the most computationally efficient and interpretable. The probabilistic MvG-SINDyC surrogate further enables uncertainty quantification and exhibits the largest computational gains under AL.