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A wax motor is a linear actuator that converts thermal energy into mechanical energy by exploiting the phase-change expansion of waxes, providing smooth and gentle actuation in various applications.
This paper proposes an active learning framework to couple high-fidelity Modelica simulations with simpler surrogate models (SINDyC, FNN, GRU) for creating efficient digital twins of thermal energy distribution systems. The approach significantly reduces the number of simulation trajectories needed while maintaining predictive accuracy and enabling uncertainty quantification.