AI 中文总结
本综述梳理了纳米光子学领域AI从机器学习到基础模型的发展，介绍了相关平台、问题与方法，分析了当前基础模型的局限并展望了多模态模型的未来方向。
AI 中文摘要
人工智能（AI）正越来越多地被用于纳米光子学系统的建模、设计与研究。本综述梳理了该领域从经典机器学习、深度学习到生成模型、迁移学习、Transformer及新兴基础模型的发展历程。首先介绍了主要的纳米光子学平台，包括纳米颗粒、纳米孔、超表面、光子晶体、多层薄膜及集成光子器件，以及它们的主要正问题与逆问题。随后综述了用于预测光学光谱与场、根据目标响应生成结构、通过优化改进设计、考虑制造约束的数据驱动方法。生成模型被视为为非唯一逆问题生成多个有效解的途径，而迁移学习、少样本学习和物理感知训练有助于减少数据需求并提升泛化能力。近期的特定领域基础模型表明，不同光学结构与响应可在共享表征中处理，但当前系统在范围和物理基础上仍存在局限。未来进展将依赖于多模态模型，其需关联几何、材料、光谱、电磁场（EM）、制造数据、实验及科学文献，并具备可靠的模拟与验证工具。当前基础模型仍为特定领域，将其扩展至更广泛的纳米光子学任务需要更强的物理基础与验证。
英文摘要
Artificial intelligence (AI) is increasingly used to model, design, and study nanophotonic systems. This review traces the development of the field from classical machine learning and deep learning to generative models, transfer learning, transformers, and emerging foundation models. It first introduces major nanophotonic platforms, including nanoparticles, nanoholes, metasurfaces, photonic crystals, multilayer thin films, and integrated photonic devices, together with their main forward and inverse problems. It then reviews data-driven methods for predicting optical spectra and fields, generating structures from target responses, improving designs through optimization, and accounting for fabrication constraints. Generative models are discussed as a way to produce multiple valid solutions to nonunique inverse problems, while transfer learning, few-shot learning, and physics-aware training help reduce data requirements and improve generalization. Recent domain-specific foundation models show that different optical structures and responses can be handled within shared representations, but current systems remain limited in scope and physical grounding. Future progress will depend on multimodal models that connect geometry, materials, spectra, electromagnetic (EM) fields, fabrication data, experiments, and scientific literature with reliable simulation and validation tools. Current foundation models remain domain-specific, and their extension to broader nanophotonic tasks will require stronger physical grounding and validation.
