This chapter reviews digital twin-based simulation modeling methods for aero engines, which integrate physical model-driven and data-driven approaches to achieve high-fidelity simulations of aero engine behavior mechanisms. This method first establishes a performance mechanism model of the target engine and uses bench test and flight data to adaptively refine it using machine learning algorithms to obtain a digital twin model for simulation. This model can reflect the physical characteristics and dynamic behavior of the engine, thereby enabling precise monitoring and prediction of engine performance. In addition, this method combines artificial intelligence algorithms to analyze the modeling bias compensation mechanism, thereby improving the reliability of aero engine simulation and providing strong support for engine performance optimization, performance prediction, and fault diagnosis.