论文标题
元素逆设计的量子启发的概率模型
A Quantum-Inspired Probabilistic Model for the Inverse Design of Meta-Structures
论文作者
论文摘要
在量子力学中，可以将标准平方波函数解释为描述要在给定位置或动量中测量粒子的可能性的概率密度。该统计特性是微观群的核心。同时，机器学习材料的逆设计引起了密集的关注，从而为物质工程提供了各种智能系统。在这里，受量子理论的启发，我们提出了一个概率的深度学习范式，用于功能元结构的逆设计。我们的基于概率密度的神经网络（PDN）可以准确捕获所有合理的元结构以满足所需的性能。概率密度分布的局部最大值对应于最可能的候选者。我们通过为每个目标传输频谱设计多个元结构来验证这种方法，以丰富设计选择。
In quantum mechanics, a norm squared wave function can be interpreted as the probability density that describes the likelihood of a particle to be measured in a given position or momentum. This statistical property is at the core of the microcosmos. Meanwhile, machine learning inverse design of materials raised intensive attention, resulting in various intelligent systems for matter engineering. Here, inspired by quantum theory, we propose a probabilistic deep learning paradigm for the inverse design of functional meta-structures. Our probability-density-based neural network (PDN) can accurately capture all plausible meta-structures to meet the desired performances. Local maxima in probability density distribution correspond to the most likely candidates. We verify this approach by designing multiple meta-structures for each targeted transmission spectrum to enrich design choices.
