arXiv Machine Learning By Zhangyong Liang, Jie Hou, Huanhuan Gao, Manyu Xiao

Categorical Optimization with Bayesian Anchored Latent Trust Regions for Structural Design under High-Dimensional Uncertainty

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arXiv:2604. 25241v2 Announce Type: replace Abstract: Categorical structural optimization under aleatoric uncertainty is challenging because each design variable must be selected from a finite catalog of admissible instances, while each candidate design may require expensive stochastic finite-element evaluations.

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arXiv Machine Learning
Aug 20

A FEM-Based Surrogate Modelling and Optimization Framework for Physics-Constrained Electromagnetic Coil Design

The paper presents a surrogate‑assisted optimization framework for designing a seven‑parameter current‑excited electromagnetic coil, coupling a 2‑D axisymmetric FEM model with a Matern 5/2 Gaussian‑process surrogate. Sequential Bayesian optimization using expected improvement (EI) is compared with COBYLA and BOBYQA, showing that the ranking of methods depends on the FEM evaluation budget and that different methods excel at early progress, terminal response, or computational cost. A retrospective study indicates no clear advantage of EI over posterior‑mean ranking on this smooth response surface, and the results are specific to the axisymmetric benchmark used.

By Yucheng Liu
arXiv AI
Aug 25

Closed-loop AI achieves certifiable engineering design

arXiv:2608.21976v1 Announce Type: new Abstract: Agentic AI has automated parts of scientific discovery, including paper generation, expert-level coding, therapeutic proposal, and autonomous experimen...

By Tianyi Yu, Chengxing Tao, Haoxuan Shen, Huiyang Li, Rugang Chen, Long Teng, Lilin Wang, Yan Li, Qingbin Chen, Chaogang Xu, Lizhong Wang