Optimizing Collaboration of LLM based Agents for Finite Element Analysis

• URL: http://arxiv.org/abs/2408.13406v1
• Date: Fri, 23 Aug 2024 23:11:08 GMT
• Title: Optimizing Collaboration of LLM based Agents for Finite Element Analysis
• Authors: Chuan Tian, Yilei Zhang,
• Abstract summary: This paper investigates the interactions between multiple agents within Large Language Models (LLMs) in the context of programming and coding tasks. We utilize the AutoGen framework to facilitate communication among agents, evaluating different configurations based on the success rates from 40 random runs for each setup.
• Score: 1.5039745292757671
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper investigates the interactions between multiple agents within Large Language Models (LLMs) in the context of programming and coding tasks. We utilize the AutoGen framework to facilitate communication among agents, evaluating different configurations based on the success rates from 40 random runs for each setup. The study focuses on developing a flexible automation framework for applying the Finite Element Method (FEM) to solve linear elastic problems. Our findings emphasize the importance of optimizing agent roles and clearly defining their responsibilities, rather than merely increasing the number of agents. Effective collaboration among agents is shown to be crucial for addressing general FEM challenges. This research demonstrates the potential of LLM multi-agent systems to enhance computational automation in simulation methodologies, paving the way for future advancements in engineering and artificial intelligence.

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