Physical Information

Physical information, encompassing the integration of physical laws and principles into data-driven models, aims to improve the accuracy, robustness, and generalizability of machine learning in various applications. Current research focuses on incorporating physical priors into neural networks, employing techniques like physics-informed neural networks and cooperative learning frameworks, as well as leveraging physical models to guide data generation and analysis (e.g., using Gaussian representations for 3D object modeling). This interdisciplinary approach holds significant promise for advancing fields such as robotics, environmental modeling, material science, and medical imaging by enhancing the reliability and interpretability of AI systems in complex real-world scenarios.