AN AI-DRIVEN PHYSICS RESEARCH CASE: MAGNETIC-MECHANICAL OSCILLATOR

This paper is based on the 6th question of the 2023 Chinese Physics Academic Competition, exploring the characteristics of a magnetic mechanical oscillator. This oscillator consists of two leaf springs fixed on a non-magnetic base and magnets attached to the upper ends of the springs. Due to the repulsive force between the magnets, the vibration of the two leaf springs is coupled. Through experimental observation and data collection, this paper thoroughly analyzes the oscillation phenomenon of the system. With the assistance of artificial intelligence (AI) algorithms, we attempt to directly derive the system's equations of motion from experimental data and compare them with theoretical analysis results. The results show that the AI algorithm can quickly and accurately fit the equations of motion without requiring complex theoretical analysis, demonstrating its potential in dealing with complex physical systems and greatly improving research efficiency. This validates the feasibility of using datadriven methods to study complex oscillatory systems and provides valuable insights for introducing AI-assisted teaching in university physics experiments.