Investigate hybrid discrepancy learning

Background: Machine learning models for battery health prediction require careful calibration of domain adaptation hyperparameters when target domain labels are unavailable for optimization.

Question / Future Work: The study will explore the use of hybrid discrepancy-learning strategies specifically tailored for electrochemical models to potentially improve physics-informed SOH forecasting and enhance generalization performance when the model encounters a domain shift.