"A LLM-Based Multi-Agent System for Motor Design Optimization Using an Uncertainty-Aware FEA- AI Hybrid Approach"

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. To address these limitations, we propose an end-to-end automated IPMSM design optimization framework that integrates retrieval-augmented generation (RAG) for structured problem definition with an uncertainty-aware FEA-AI hybrid optimization pipeline.

Reference:

• Han, J., Yang, S., & Kang, N. (2026). A Multi-Agent System for IPMSM Design Optimization via an FEA-AI Hybrid Approach. arXiv preprint arXiv:2606.09037.

 Last updated: Jun. 2026. 

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