Learning Design Skills as Memory Policies for Agentic Photonic Inverse Design
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-depe...
| Authors | Shengchao Chen, Ting Shu, Sufen Ren |
|---|---|
| Primary category | cs.CL |
| Published | 28 May 2026 |
| Last updated at source | 28 May 2026 |
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