The data-driven approach, while powerful, has limitations that can be addressed by incorporating first-principles physics models. One limitation is the potential for overfitting to the training data, leading to reduced generalization to unseen scenarios. By integrating physics-based models, the system can benefit from known physical laws and constraints, improving the model's interpretability and robustness. Physics models can also help in situations where data is scarce or noisy, providing a reliable foundation for predictions. Additionally, physics-informed machine learning approaches can blend data-driven insights with domain knowledge, enhancing the model's accuracy and reliability.