A Hybrid Finite Element–Deep Learning Framework for Bearing Structure Optimization
A hybrid optimization framework integrating a corrected two-dimensional axisymmetric finite element method (2D-AxFEM) model, a deep learning surrogate model, and a genetic algorithm that enables efficient exploration of multi-physics design spaces and provides a scalable solution for high-speed bearing optimization.