Large Language Models as Optimization Controllers: Adaptive Continuation for SIMP Topology Optimization

1. 1Use a large language model to receive structured observations at each iteration
2. 2Output numerical parameters based on current state
3. 3Replace conventional fixed-schedule continuation with real-time, state-conditioned parameter decisions
4. 4Evaluate the performance of the LLM controller using metrics such as compliance and volume fraction

Researchers and engineers working on topology optimization and AI applications can benefit from this approach, as it enables more efficient and adaptive optimization processes

💡 Large language models can be used to make adaptive decisions in optimization problems, improving efficiency and performance

💡 LLMs can control SIMP topology optimization in real-time!