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This paper presents a novel deep learning approach to predict inertial lift forces in microfluidic devices without explicit geometric parameters, enabling better generalization to unseen channel cross-sections compared to previous models.
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.