The field of design optimization is undergoing significant transformations with the advent of Large Language Models (LLMs). Recent developments indicate a shift towards leveraging LLMs to generate and optimize algorithms, with a focus on iterative refinement and in-context learning. This approach has shown promise in addressing complex optimization problems, including those in nuclear engineering and photonic structure design. Noteworthy papers in this area include 'Optimization through In-Context Learning and Iterative LLM Prompting for Nuclear Engineering Design Problems', which demonstrates the effectiveness of LLMs in optimizing nuclear fuel assembly configurations, and 'Optimizing Photonic Structures with Large Language Model Driven Algorithm Discovery', which showcases the potential of LLM-generated algorithms in solving optical design tasks. Overall, the field is moving towards increased adoption of LLMs in design optimization, with a focus on developing more efficient and effective methods for leveraging these powerful tools.
Advances in Large Language Model-Driven Design Optimization
Sources
Code Evolution Graphs: Understanding Large Language Model Driven Design of Algorithms
Optimization through In-Context Learning and Iterative LLM Prompting for Nuclear Engineering Design Problems
Optimizing Photonic Structures with Large Language Model Driven Algorithm Discovery
Can We Make Code Green? Understanding Trade-Offs in LLMs vs. Human Code Optimizations
Sociotechnical Effects of Machine Translation
From User Preferences to Optimization Constraints Using Large Language Models
Generative Reliability-Based Design Optimization Using In-Context Learning Capabilities of Large Language Models