Physic Data Hybrid Dynamic Model

Physics-data hybrid dynamic models integrate physical principles with data-driven machine learning to create more accurate and efficient models of complex systems. Current research focuses on combining physics-based equations (e.g., Lagrangian mechanics, partial differential equations) with various machine learning architectures, such as neural networks (including recurrent and physics-informed networks), XGBoost, and LSTM, to improve model performance and reduce data requirements. This approach is proving valuable across diverse fields, enhancing the accuracy and efficiency of simulations for applications ranging from robotics and industrial process control to electrical machine design and bioreactor modeling. The resulting hybrid models offer a powerful alternative to purely physics-based or purely data-driven approaches, particularly when dealing with systems exhibiting high nonlinearity or incomplete physical understanding.