用AI推导光纤通信公式，准确率接近传统模型。
Mathematical Reasoning Enhanced LLM for Formula Derivation: A Case Study on Fiber NLI Modellin
- 用结构化提示引导大模型进行符号物理推理。
- 推导出多跨段C/C+L波段的新近似公式，误差低于0.109 dB。
- 适合需要快速建模的光通信研究人员使用。
大型语言模型（LLMs）在代码生成和文本合成方面表现突出，但在特定科学领域中的符号物理推理潜力尚未充分探索。本文提出一种增强数学推理能力的生成式AI方法，用于光纤非线性干扰建模中的公式推导。通过结构化提示引导，成功重构了已知的闭合形式ISRS GN表达式，并进一步推导出适用于多跨段C与C+L波段传输的新近似模型。数值验证表明，该模型在所有通道和跨段上的平均绝对误差低于0.109 dB，中心信道的GSNR与基准模型几乎一致，展现出良好的物理一致性与实际准确性。
原文摘要 · Abstract (English)
Recent advances in large language models (LLMs) have demonstrated strong capabilities in code generation and text synthesis, yet their potential for symbolic physical reasoning in domain-specific scientific problems remains underexplored. We present a mathematical reasoning enhanced generative AI approach for optical communication formula derivation, focusing on the fiber nonlinear interference modelling. By guiding an LLM with structured prompts, we successfully reconstructed the known closed-form ISRS GN expressions and further derived a novel approximation tailored for multi-span C and C+L band transmissions. Numerical validations show that the LLM-derived model produces central-channel GSNRs nearly identical to baseline models, with mean absolute error across all channels and spans below 0.109 dB, demonstrating both physical consistency and practical accuracy.
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