Posted November 26, 2025
In May 2025 Yiming Fan successfully defended his PhD thesis in the Department of Mechanical, Aerospace, and Nuclear Engineering at Rensselaer Polytechnic Institute. Yiming Fan developed a comprehensive data-driven structural health monitoring (SHM) framework that combines deep learning, signal processing, and statistical modeling to tackle both forward and inverse damage assessment problems. By compressing high-dimensional guided-wave and vibration data, fusing multiple sensing modalities, and integrating multi-fidelity Gaussian process models that blend simulations with limited experimental data, his work enables faster, more accurate, and more robust damage detection in complex, noisy, and uncertain aerospace and mechanical structures.
