Deep Learning has been a critical part of designing inverse design methods
that are computationally efficient and accurate. An example of this is the
design of photonic metasurfaces by using their photoluminescent spectrum as the
input data to predict their topology. One fundamental challenge of these
systems is their ability to represent nonlinear relationships between sets of
data that have different dimensionalities. Existing design methods often
implement a conditional Generative Adversarial Network in order to solve this
problem, but in many cases the solution is unable to generate structures that
provide multiple peaks when validated. It is demonstrated that in response to
the target spectrum, the Bidirectional Adversarial Autoencoder is able to
generate structures that provide multiple peaks on several occasions. As a
result the proposed model represents an important advance towards the
generation of nonlinear photonic metasurfaces that can be used in advanced
metasurface design.

使用深度学习设计反向设计方法，以预测拓扑结构的光致发光光谱作为输入数据，采用双向对抗自编码器能够生成能够提供多个峰值的非线性光子元表面，对于先进光子元表面设计具有重要的推进作用。

双向对抗自编码器用于光热表面结构设计

Bidirectional Adversarial Autoencoders for the design of Plasmonic  Metasurfaces

Subwavelength photonic structures and metamaterials provide revolutionary
approaches for controlling light. The inverse design methods proposed for these
subwavelength structures are vital to the development of new photonic devices.
However, most of the existing inverse design methods cannot realize direct
mapping from optical properties to photonic structures but instead rely on
forward simulation methods to perform iterative optimization. In this work, we
exploit the powerful generative abilities of artificial intelligence (AI) and
propose a practical inverse design method based on latent diffusion models. Our
method maps directly the optical properties to structures without the
requirement of forward simulation and iterative optimization. Here, the given
optical properties can work as "prompts" and guide the constructed model to
correctly "draw" the required photonic structures. Experiments show that our
direct mapping-based inverse design method can generate subwavelength photonic
structures at high fidelity while following the given optical properties. This
may change the method used for optical design and greatly accelerate the
research on new photonic devices.

基于潜在扩散模型，利用人工智能的生成能力，提出了一种不依赖前向模拟和迭代优化，直接将光学性质映射到结构的逆向设计方法。该方法可按照给定的光学性质生成高保真度的次波长光子结构，可能改变光学设计方法，并加速新光子器件的研究。