Achieving broadband electromagnetic absorption in laminated composites through progressive Bayesian optimization

As radar detection technology advances, achieving broadband electromagnetic (EM) absorption in laminated composites remains a critical challenge due to the intricate nonlinear coupling between structures/compositions and frequency-dependent EM responses, which demands specialized expertise and extensive trial-and-error efforts. Here, this paper presents a data-driven machine learning (ML) framework based on progressive Bayesian optimization (PBO), which addresses traditional Bayesian optimization's (BO) limitations to local optima. Within this framework, a predictive model is trained iteratively for EM performance of variable ply configurations, which exhibits desirable robustness and generalization ability.…

• NN
  National Natural Science Foundation of China

  Awards: 52303116, 52403125
• NS
  Natural Science Foundation of Hunan Province

  Award: 2024JJ6461
• SA
  Science and Technology Program of Hunan Province

  Award: 2022RC1080