This research integrates Large Language Models (LLMs) with metaheuristic algorithms to create a powerful generative approach for reliability-based design optimization.

• Harnesses LLMs' in-context learning capabilities to process limited historical information
• Combines AI pattern recognition with iterative search techniques
• Creates more efficient and innovative engineering design solutions
• Demonstrates practical applications in reliability-based design optimization

For engineering teams, this approach represents a significant advancement in how complex design challenges can be solved, potentially reducing optimization time while improving design reliability under uncertainty.

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