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

Abstract Large language models (LLMs) have demonstrated remarkable in-context learning (ICL) capabilities, enabling flexible utilization of limited historical information to play pivotal roles in reasoning, problem-solving, and complex pattern recognition tasks. Inspired by the successful applications of LLMs in multiple domains, this article proposes a generative design approach by leveraging the ICL capabilities of LLMs with the iterative search mechanisms of metaheuristic algorithms for solving reliability-based design optimization (RBDO) problems. In detail, Kriging surrogate modeling is employed to replace the expensive simulations, and thus Monte Carlo simulation (MCS) can be used to approximate the probability of failure for design alternatives. Then, an RBDO-informed LLM prompt is designed to dynamically provide critical information to the LLMs, which enables the rapid generation of new high-quality design points that satisfy the reliability constraints while improving design efficiency. With the LLMs as a design generator, the RBDO is an iterative process to obtain feasible design solutions with improved performance. With the Deepseek-V3 model, three case studies are used to demonstrate the performance of the proposed approach for solving RBDO problems. The results indicate that the proposed LLM-based generative RBDO approach successfully identifies feasible solutions that meet reliability constraints while achieving a comparable convergence rate compared to traditional genetic algorithms.