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

Closed-loop AI achieves certifiable engineering design

Read the original on arXiv AI →

The Flow has not summarised this story yet — read it at arXiv AI.

arXiv AI
Aug 3

A Multi-Agent System for Motor Design Optimization via an FEA-AI Hybrid Approach

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 AI
Jun 9

A Multi-Agent System for IPMSM Design Optimization via an FEA-AI Hybrid Approach

arXiv:2606. 09037v1 Announce Type: new Abstract: Interior permanent magnet synchronous motor (IPMSM) design requires balancing conflicting objectives and multi-physics constraints, while modern optimization workflows face three bottlenecks: manual problem setup, high finite element analysis (FEA) cost, and unreliable surrogate-based search in sparse or out-of-distribution regions.

By Jinseong Han, Sunwoong Yang, Namwoo Kang
arXiv AI
Jul 13

LLM-Driven Evolutionary Generation of Multi-Objective Bayesian Optimization Algorithms

arXiv:2607. 08791v1 Announce Type: cross Abstract: Designing effective multi-objective Bayesian optimization (MOBO) algorithms requires balancing many interdependent design choices whose optimal configuration is problem-dependent and typically demands deep expertise.

By Georgios Laskaris, Reuben Brasher, Niki van Stein, Elena Raponi, Thomas B\"ack, Florian Neukart
arXiv AI
Aug 11

WuYuEval: A Multi-Level Benchmark for Large Language Models in Solid Waste Management

arXiv:2608. 07529v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as technical assistants, but their competence in solid waste management (SWM) remains difficult to assess because existing benchmarks emphasize general knowledge rather than professional decisions under engineering, environmental, and policy constraints.

By Yi Zhang, Hongyang Wang, Zheng Hao Leong, Zihao Wu, Kaijun Lin, Zhixing Pan, Qixun Huangfu, Wei Ren, Wenyan Wu, Fangyun Wang, Wenting Yu, Hengyu Lin, Muling Yang, Zongguo Wen