Data- and mechanism-driven scientific computing analysis of artificial intelligence
基于数据和机理驱动的人工智能科学智算分析

摘要

当前，人工智能技术的发展迅速，其在科学计算中的应用尤为重要。本研究围绕数据驱动与机理驱动的人工智能技术在科学计算领域的应用展开探讨，旨在提出一种融合数据和机理驱动的智能科学计算方法。首先，分析传统的科学计算方法与机理模型的局限性，指出单一驱动模型无法充分利用已有数据与领域知识的问题。接着，通过构建数据驱动的人工智能模型，结合机理驱动的物理规律，提高模型的预测精度和泛化能力。实验结果表明，所提方法在多个科学计算案例中表现出较传统方法更优的准确率和效率。最后，探讨了该方法在未来科研中的应用前景和潜在挑战。研究结果表明，基于数据和机理驱动的综合分析框架，为解决复杂科学问题提供了新的思路和工具，具有重要的理论意义和应用价值。这篇论文的生成，基于对数据和机理两方面的综合利用，有助于推动科学计算领域的深入研究和发展。

Abstract

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.