Yaxuan Li studies electromagnetic nondestructive testing and physics-informed neural networks, inverting metal defects from sparse magnetic-field observations. The work combines electromagnetic simulation, physics-constrained modeling, and data-driven methods for defect localization and geometry reconstruction, with an interest in engineering applications of intelligent sensing and inspection.
主要研究方向为电磁无损检测与物理信息神经网络，围绕稀疏磁场观测下的金属缺陷反演开展研究。结合电磁场仿真、物理约束建模与数据驱动方法，探索缺陷定位及几何形貌重建，并关注智能传感与检测技术的工程应用。