标题： 通过表征学习在纳米光子学中的逆向设计 显示英文标题

标题： Inverse Design in Nanophotonics via Representation Learning

摘要： 纳米光子学中的逆向设计，即计算发现实现特定电磁（EM）响应的结构，已成为近期光学进展的关键工具。 传统的基于直觉或迭代优化的方法在本质上高维、非凸的设计空间以及电磁模拟的大量计算需求面前面临挑战。 最近，机器学习（ML）已被有效用于解决这些瓶颈。 本综述通过表示学习的视角来阐述增强型机器学习逆向设计方法，将其分为两类：输出端方法和输入端方法。 输出端方法使用机器学习在解空间中学习一种表示，以创建可微分求解器，从而加速优化。 相反，输入端技术则利用机器学习学习可行器件几何的紧凑潜在空间表示，通过生成模型实现高效的全局探索。 每种策略在数据需求、泛化能力以及新设计发现潜力方面都有其独特的权衡。 结合物理基础优化与数据驱动表示的混合框架有助于摆脱较差的局部最优，提高可扩展性，并促进知识迁移。 最后，我们强调了开放性的挑战和机遇，重点在于复杂性管理、与几何无关的表示、制造约束的整合以及多物理场协同设计的进步。 显示英文摘要

摘要： Inverse design in nanophotonics, the computational discovery of structures achieving targeted electromagnetic (EM) responses, has become a key tool for recent optical advances. Traditional intuition-driven or iterative optimization methods struggle with the inherently high-dimensional, non-convex design spaces and the substantial computational demands of EM simulations. Recently, machine learning (ML) has emerged to address these bottlenecks effectively. This review frames ML-enhanced inverse design methodologies through the lens of representation learning, classifying them into two categories: output-side and input-side approaches. Output-side methods use ML to learn a representation in the solution space to create a differentiable solver that accelerates optimization. Conversely, input-side techniques employ ML to learn compact, latent-space representations of feasible device geometries, enabling efficient global exploration through generative models. Each strategy presents unique trade-offs in data requirements, generalization capacity, and novel design discovery potentials. Hybrid frameworks that combine physics-based optimization with data-driven representations help escape poor local optima, improve scalability, and facilitate knowledge transfer. We conclude by highlighting open challenges and opportunities, emphasizing complexity management, geometry-independent representations, integration of fabrication constraints, and advancements in multiphysics co-designs.