标题： MAPS：多保真度人工智能增强的光子仿真与逆向设计基础设施 显示英文标题

标题： MAPS: Multi-Fidelity AI-Augmented Photonic Simulation and Inverse Design Infrastructure

摘要： 逆向设计已成为光子器件优化的变革性方法，使人们能够探索高维、非直观的设计空间，以创建超紧凑器件，并推动计算和互连中的光子集成电路（PICs）的发展。 然而，实际挑战，如设备性能不佳、可制造性有限、对变化的高度敏感性、计算效率低下以及缺乏可解释性，阻碍了其在商业硬件中的采用。 人工智能辅助的光子仿真和设计的最新进展具有变革性的潜力，通过传统数值方法数倍的速度加速仿真和设计生成。 尽管有这些突破，但缺乏开源、标准化的基础设施和评估基准限制了其可访问性和跨学科合作。 为了解决这个问题，我们引入了MAPS，这是一个多保真度的人工智能增强的光子仿真和逆向设计基础设施，旨在弥合这一差距。 MAPS包含三个协同组件：(1) MAPS-Data：一个数据集获取框架，用于生成多保真度、丰富标记的器件，为光学人工智能研究提供高质量数据。 (2) MAPS-Train：一个灵活的光学人工智能训练框架，提供分层的数据加载管道、自定义模型构建、支持数据驱动和物理驱动损失以及全面的评估。 (3) MAPS-InvDes：一个先进的伴随逆向设计工具包，抽象了复杂的物理过程，但暴露了灵活的优化步骤，集成了预训练的人工智能模型，并包含了制造变化模型。 该基础设施MAPS为开发、基准测试和推进人工智能辅助的光子设计工作流提供了一个统一的开源平台，加速了光子硬件优化和科学机器学习的创新。 显示英文摘要

摘要： Inverse design has emerged as a transformative approach for photonic device optimization, enabling the exploration of high-dimensional, non-intuitive design spaces to create ultra-compact devices and advance photonic integrated circuits (PICs) in computing and interconnects. However, practical challenges, such as suboptimal device performance, limited manufacturability, high sensitivity to variations, computational inefficiency, and lack of interpretability, have hindered its adoption in commercial hardware. Recent advancements in AI-assisted photonic simulation and design offer transformative potential, accelerating simulations and design generation by orders of magnitude over traditional numerical methods. Despite these breakthroughs, the lack of an open-source, standardized infrastructure and evaluation benchmark limits accessibility and cross-disciplinary collaboration. To address this, we introduce MAPS, a multi-fidelity AI-augmented photonic simulation and inverse design infrastructure designed to bridge this gap. MAPS features three synergistic components: (1) MAPS-Data: A dataset acquisition framework for generating multi-fidelity, richly labeled devices, providing high-quality data for AI-for-optics research. (2) MAPS-Train: A flexible AI-for-photonics training framework offering a hierarchical data loading pipeline, customizable model construction, support for data- and physics-driven losses, and comprehensive evaluations. (3) MAPS-InvDes: An advanced adjoint inverse design toolkit that abstracts complex physics but exposes flexible optimization steps, integrates pre-trained AI models, and incorporates fabrication variation models. This infrastructure MAPS provides a unified, open-source platform for developing, benchmarking, and advancing AI-assisted photonic design workflows, accelerating innovation in photonic hardware optimization and scientific machine learning.