TopOptAgents deploys six LLM agents in self-refining loops to automate the full topology optimization workflow and succeeds on problem classes where single LLMs fail.
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A transformer model with self-attention and auxiliary physics losses learns a direct non-iterative mapping from loads and fields to manufacturable optimized topologies.
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Self-Refining Topology Optimization via an LLM-Based Multi-Agent Framework
TopOptAgents deploys six LLM agents in self-refining loops to automate the full topology optimization workflow and succeeds on problem classes where single LLMs fail.
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Physics-Informed Transformer for Real-Time High-Fidelity Topology Optimization
A transformer model with self-attention and auxiliary physics losses learns a direct non-iterative mapping from loads and fields to manufacturable optimized topologies.