<ul><li>Large Language Models (LLMs) have in-context learning capabilities for reasoning, problem-solving, and pattern recognition.</li><li>A generative design method is proposed that combines LLMs with metaheuristic algorithms for reliability-based design optimization.</li><li>Reliability analysis is performed using LLMs and Kriging surrogate modeling to reduce computational burden.</li><li>Experimental results demonstrate the effectiveness of the proposed approach in identifying feasible solutions.</li></ul>

Generative Reliability-Based Design Optimization Using In-Context Learning Capabilities of Large Language Models

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