如何在计算物理教学中融入人工智能的应用
How to Integrate the Application of Artificial Intelligence into Computational Physics Education

在全球人工智能的大背景下，随着数据科学和机器学习的融合，计算物理领域将越来越多地把数据科学和机器学习的方法应用于问题求解。通过数据驱动的方法和机器学习算法，计算物理可以更好地处理复杂的物理现象，并从大量的数据中提取有用的信息。如何在高校计算物理教学中融合人工智能是非常必要的，我们主要从三个方面展开指导学生熟悉计算物理中常用的计算方法；人工智能训练优化已有的计算方法；国际合作共建知识共享平台等，通过这三个方面使得计算物理与物理学、材料科学、化学和生物学等学科进行交叉与合作，共同解决复杂的科学和工程问题，推动技术创新和应用拓展。
In the context of global artificial intelligence, with the fusion of data science and machine learning, the field of computational physics will increasingly apply methods from data science and machine learning to problem-solving. Through data-driven approaches and machine learning algorithms, computational physics can better handle complex physical phenomena and extract useful information from large datasets. Integrating artificial intelligence into university-level computational physics education is highly necessary. We primarily focus on three aspects: guiding students to become familiar with commonly used computational methods in computational physics; training artificial intelligence to optimize existing computational methods; and fostering international cooperation to build knowledge-sharing platforms. Through these three aspects, computational physics can engage in cross-disciplinary collaboration with physics, materials science, chemistry, biology, and other fields, jointly addressing complex scientific and engineering problems, and promoting technological innovation and application expansion.