Residual Physic

Residual physics leverages the strengths of both physics-based models and data-driven methods to improve the accuracy and efficiency of various systems. Current research focuses on integrating physics models (e.g., Model Predictive Control, Intelligent Driver Model) with machine learning techniques (e.g., reinforcement learning, neural networks) to learn and correct for discrepancies between simulated and real-world behavior, often using residual learning architectures. This approach enhances the interpretability and generalizability of data-driven models while reducing reliance on extensive datasets, finding applications in diverse fields such as robotics, autonomous vehicle control, and medical imaging.