Agentic Risk-Aware Set-Based Engineering Design
Leveraging LLM-based multi-agent teams to automate high-stakes engineering design and risk assessment.
This paper introduces a multi-agent framework guided by Large Language Models (LLMs) to assist in the early stages of engineering design, a phase often characterized by vast parameter spaces and inherent uncertainty. Operating under a human-in-the-loop paradigm and demonstrated o...
ArXiv CS.AI
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This framework marks a significant shift toward autonomous engineering by integrating LLM-driven agents with formal risk metrics like CVaR to handle complex design spaces. By automating initial exploration while keeping human oversight, it substantially reduces the cognitive load on engineers during high-uncertainty project phases.
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