DeepAdjoint：集成数据驱动机器学习和优化算法的一体化光子逆向设计框架
DeepAdjoint: An All-in-One Photonic Inverse Design Framework Integrating Data-Driven Machine Learning with Optimization Algorithms
摘要：
近年来，结合机器学习（ML）和电磁优化算法的混合设计策略已成为光子结构和器件逆向设计的新范式。虽然经过训练的数据驱动的神经网络可以快速识别给定数据集设计空间的全局最优解，但迭代优化算法可以进一步细化解决方案并克服数据集限制。此外，这种混合ML优化方法可以降低计算成本并加快新电磁部件的发现。然而，现有的混合ML优化方法尚未在单个集成和用户友好的环境中优化材料和几何形状。此外，由于获取ML的大型数据集的挑战，以及为光子设计训练的孤立模型的指数增长，需要标准化ML优化工作流程，同时使预训练的模型易于访问。受这些挑战的启发，我们在这里介绍DeepAdjoint，这是一个通用，开源和多目标的“一体化”全球光子学逆向设计应用框架，它将预训练的深度生成网络与最先进的电磁优化算法（如伴随变量方法）集成在一起。DeepAdjoint允许设计人员指定任意光学设计目标，然后获得对制造公差具有鲁棒性并具有所需光学特性的光子结构-所有这些都在单个用户引导的应用界面中。因此，我们的框架铺平了道路ML和光子逆向设计的优化算法的系统统一。
英文摘要：
In recent years, hybrid design strategies combining machine learning (ML) with electromagnetic optimization algorithms have emerged as a new paradigm for the inverse design of photonic structures and devices. While a trained, data-driven neural network can rapidly identify solutions near the global optimum with a given dataset's design space, an iterative optimization algorithm can further refine the solution and overcome dataset limitations. Furthermore, such hybrid ML-optimization methodologies can reduce computational costs and expedite the discovery of novel electromagnetic components. However, existing hybrid ML-optimization methods have yet to optimize across both materials and geometries in a single integrated and user-friendly environment. In addition, due to the challenge of acquiring large datasets for ML, as well as the exponential growth of isolated models being trained for photonics design, there is a need to standardize the ML-optimization workflow while making the pre-trained models easily accessible. Motivated by these challenges, here we introduce DeepAdjoint, a general-purpose, open-source, and multi-objective "all-in-one" global photonics inverse design application framework which integrates pre-trained deep generative networks with state-of-the-art electromagnetic optimization algorithms such as the adjoint variables method. DeepAdjoint allows a designer to specify an arbitrary optical design target, then obtain a photonic structure that is robust to fabrication tolerances and possesses the desired optical properties - all within a single user-guided application interface. Our framework thus paves a path towards the systematic unification of ML and optimization algorithms for photonic inverse design.
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