Advances in International Computer Science

Currently, the development of artificial intelligence technology is rapid, and its application in scientific computing is particularly important. This study focuses on the application of data-driven and mechanism-driven artificial intelligence technologies in the field of scientific computing, aiming to propose an intelligent scientific computing method that integrates data and mechanism-driven approaches. Firstly, analyze the limitations of traditional scientific computing methods and mechanism models, and point out the problem that a single driving model cannot fully utilize existing data and domain knowledge. Then, by constructing data-driven artificial intelligence models and integrating mechanism-driven physical laws, the prediction accuracy and generalization ability of the models can be enhanced. The experimental results show that the proposed method demonstrates better accuracy and efficiency than traditional methods in multiple scientific computing cases. Finally, the application prospects and potential challenges of this method in future scientific research were discussed. The research results show that the comprehensive analysis framework driven by data and mechanism provides new ideas and tools for solving complex scientific problems, and has important theoretical significance and application value. The generation of this paper, based on the comprehensive utilization of both data and mechanisms, is conducive to promoting in-depth research and development in the field of scientific computing.

Artificial Intelligence; Scientific computing; Data-driven; Mechanism-driven; Intelligent scientific computing methods