Physics and Data Cooperative Modeling in Transportation Cyber-Physical System: Review and Perspectives

This research provides a comprehensive examination of hybrid modeling approaches that integrate physics-based and data-driven methods across three transportation sectors: automotive, aviation, and maritime. Motivated by the need for improved generalization, interpretability, and robustness, hybrid models combine the expressiveness of machine learning with the reliability of physical laws. Existing hybridization techniques are categorized according to the fusion point, including input features, loss functions, model architecture, and output correction, and their respective advantages and challenges are assessed. Domain-specific trends are identified: automotive and aviation benefit from high-fidelity physical models and abundant labeled datasets, while maritime applications often contend with sparse, noisy data and low-fidelity models, thus placing greater emphasis on hybrid methods to ensure safety and reliability. We further analyze strategies such as physics-informed neural networks, residual learning, transfer learning with synthetic data, and multimodel architectures, and evaluate their suitability under different data availability and physical modeling constraints. This review highlights the critical role of domain knowledge, the impact of physical model fidelity, and the importance of adaptive integration strategies in achieving robust and trustworthy dynamic models in real-world transport systems.