Physic Based

Physics-based modeling integrates physical laws and principles into machine learning models to improve accuracy, efficiency, and interpretability, particularly in scenarios with limited data. Current research focuses on developing hybrid models combining neural networks (e.g., PINNs, DEQs) with traditional numerical methods or incorporating physical constraints into various architectures like graph neural networks and transformers. This approach enhances the reliability and generalizability of machine learning for applications ranging from robotics and character animation to weather prediction and material science, bridging the gap between data-driven and physics-driven approaches.