As computing capabilities surge forward and artificial intelligence progresses significantly, the integration of AI into mechanical simulation is expanding at a notable rate, substantially boosting advancements in engineering and scientific investigation realms. This article mainly explores three types of applications of artificial intelligence in mechanical simulation: surrogate models, physics-informed neural networks (PINNs), and adaptive grid refinement. These technologies improve the efficiency and accuracy of simulation and open up new research directions and application areas. In summary, the application of artificial intelligence technology in mechanical simulation has made significant progress. Still, it faces challenges such as strong data dependence, limited model generalization ability, and optimization of computational efficiency. Future research needs to conduct in-depth exploration into improving algorithm efficiency, enhancing model generalization ability, and expanding application scenarios. In addition, strengthening the combination of theoretical research and practical application will be an important direction for promoting the sustainable development of this field.
