Learning Design Skills as Memory Policies for Agentic Photonic Inverse Design
For researchers in photonic inverse design, this work introduces a memory-skill learning paradigm that improves iterative design efficiency, though it is domain-specific and incremental.
The paper tackles photonic crystal fiber inverse design under expensive simulation, proposing SkillPCF which uses memory-policy learning to accumulate reusable design knowledge. It achieves stronger design-quality and efficiency trade-offs across multiple LLM backbones and baselines.
Photonic crystal fiber (PCF) inverse design remains challenging because candidate geometries must satisfy coupled optical targets under expensive electromagnetic simulation. Existing pipelines improve surrogate prediction or one-shot parameter recommendation, but they do not accumulate reusable design knowledge across iterative trials. We formulate PCF inverse design as a memory-policy learning problem and propose SkillPCF, a closed-loop agent framework that combines a physics-guided memory skill bank, reinforcement-learned skill selection, and simulator-grounded skill evolution. We further construct a real-world dataset with 479 expert interaction traces (2,507 spans) and 553 memory-dependent evaluation queries covering dispersion engineering, loss optimization, and multi-objective design. Experiments across multiple LLM backbones and classical baselines show that SkillPCF achieves stronger design-quality and efficiency trade-offs under practical simulation budgets, demonstrating the effectiveness of our proposed memory-skill learning paradigm for physics-aware PCF inverse design.