College Physics ›› 2026, Vol. 45 ›› Issue (6): 79-.doi: 10.16854/j.cnki.1000-0712.250368

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Teaching design for integrating physics courses with neural networks

SUN Linfeng, LI Jungang   

  1. School of Physics, Beijing Institute of Technology, Beijing 100081, China
  • Received:2025-07-16 Accepted:2025-10-31 Online:2026-08-01 Published:2026-08-28

Abstract: In recent years, artificial intelligence technology has developed rapidly. The 2024 Nobel Prize in Physics was awarded to scientists for their groundbreaking contributions in the fields of artificial neural networks and machine learning, further underscoring the deep connection between physics and cutting-edge technologies. This paper explores a teaching design that integrates neural network technology into physics courses, aiming to enrich course content, enhance the cutting-edge nature and technological relevance of the course, stimulate student interest, and ultimately improve the quality and effectiveness of physics courses. This study emphasizes the integration potential of neural networks with the physics knowledge system, providing a reference for the innovation and expansion of physics course content.


Key words: artificial intelligence, neural networks, physics courses, teaching design