LLMs now optimize engineering designs by learning from past performance data to suggest better shapes.
LLM-PSO combines LLMs with evolutionary strategy to optimize complex shape designs, achieving faster convergence and better performance than traditional methods.
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https://arxiv.org/abs/2412.08072
Original Problem 🤔:
Shape optimization in engineering requires complex algorithms and significant computational resources. Traditional methods like genetic algorithms and reinforcement learning are slow to converge and often get stuck in local optima.
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Solution in this Paper 🔧:
→ Introduces LLM-PSO, a framework that uses LLMs to guide evolutionary optimization for shape design.
→ Uses few-shot prompting with 5 components: task assignment, design vector dimensions, parameter range, performance records, and output format.
→ Evolves population of N designs generation by generation following Gaussian distribution.
→ LLM suggests promising mean values for next generation based on previous performance data.
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Key Insights 💡:
→ LLMs can effectively guide optimization without explicit retraining
→ Performance degrades with higher degrees of freedom (>4)
→ Initial optimization phase shows slower progress than genetic algorithms
→ Works best for problems with moderate complexity
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Results 📊:
→ 10x faster convergence compared to reinforcement learning for simple cases
→ Matched theoretical optimal solutions for 3D axisymmetric body optimization
→ Achieved similar or better results than reinforcement learning in airfoil design
→ Shows lower statistical variance for simpler cases
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