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MetaKoopman proposes a Bayesian meta-learning framework for modeling nonlinear dynamics using linear latent representations via Koopman operators, enabling closed-form updates and uncertainty quantification. It is validated on autonomous truck and trailer systems under adverse winter conditions, outperforming prior methods in prediction accuracy and robustness.