arXiv:2606. 09037v2 Announce Type: replace Abstract: This study presents a large language model (LLM)-based multi-agent framework for interior permanent magnet synchronous motor (IPMSM) design optimization that mitigates limitations of conventional workflows: expertise-dependent problem setup and data preparation, the prohibitive computational cost of finite element analysis (FEA), and the unreliability of AI surrogates in unexplored regions.
By Jinseong Han, Sunwoong Yang, Namwoo Kang
arXiv:2606. 00862v1 Announce Type: cross Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for expensive black-box optimization problems.
By Xiao Jin, Yongxiong Wang, Haobo Liu, Yudong Du, Yukun Du
arXiv:2605. 08756v2 Announce Type: replace Abstract: Automatic heuristic design (AHD) has emerged as a promising paradigm for solving NP-hard combinatorial optimization problems (COPs).
By Haoze Lv, Ning Lu, Ziang Zhou, Yew-Soon Ong, Shengcai Liu
AI for Research (AI4Research) leverages AI to automate and improve scientific workflows. While experimental design is a critical stage of the research process, prior work has focused primarily on code implementation and execution, overlooking the importance of this stage, and no benchmark exists to evaluate AI's ability to conduct systematic experiment design.
arXiv:2608. 03501v1 Announce Type: new Abstract: AI for Research (AI4Research) leverages AI to automate and improve scientific workflows.
By Zejun Liu, Jian Wu, Ru Peng, Yuliang Ji, Dongyuan Li, Renhe Jiang, Yue Zhang
arXiv:2606. 07463v1 Announce Type: cross Abstract: Pre-layout design space exploration (DSE) for high-speed signal integrity (SI) analysis is often limited by the computational cost of simulations and iterative optimization algorithms within modern electronic design automation (EDA) workflows.
By Julian With\"oft, Werner John, Emre Ecik, Ralf Br\"uning, J\"urgen G\"otze