Metamaterials are artificial materials designed to exhibit effective material
parameters that go beyond those found in nature. Composed of unit cells with
rich designability that are assembled into multiscale systems, they hold great
promise for realizing next-generation devices with exceptional, often exotic,
functionalities. However, the vast design space and intricate
structure-property relationships pose significant challenges in their design. A
compelling paradigm that could bring the full potential of metamaterials to
fruition is emerging: data-driven design. In this review, we provide a holistic
overview of this rapidly evolving field, emphasizing the general methodology
instead of specific domains and deployment contexts. We organize existing
research into data-driven modules, encompassing data acquisition, machine
learning-based unit cell design, and data-driven multiscale optimization. We
further categorize the approaches within each module based on shared
principles, analyze and compare strengths and applicability, explore
connections between different modules, and identify open research questions and
opportunities.

介绍了数据驱动设计范例这一概念，并将现有的研究分为数据获取、基于机器学习的单元格设计、数据驱动的多尺度优化三个模块进行了综述，分析比较了各个模块的优势和适用性，并探讨了模块之间的联系。

基于数据驱动的超材料及多尺度系统设计综述

Data-Driven Design for Metamaterials and Multiscale Systems: A Review

Coordinate networks like Multiplicative Filter Networks (MFNs) and BACON
offer some control over the frequency spectrum used to represent continuous
signals such as images or 3D volumes. Yet, they are not readily applicable to
problems for which coarse-to-fine estimation is required, including various
inverse problems in which coarse-to-fine optimization plays a key role in
avoiding poor local minima. We introduce a new coordinate network architecture
and training scheme that enables coarse-to-fine optimization with fine-grained
control over the frequency support of learned reconstructions. This is achieved
with two key innovations. First, we incorporate skip connections so that
structure at one scale is preserved when fitting finer-scale structure. Second,
we propose a novel initialization scheme to provide control over the model
frequency spectrum at each stage of optimization. We demonstrate how these
modifications enable multiscale optimization for coarse-to-fine fitting to
natural images. We then evaluate our model on synthetically generated datasets
for the the problem of single-particle cryo-EM reconstruction. We learn high
resolution multiscale structures, on par with the state-of-the art.

通过引入跳连接和新的初始化方案，我们提出了一种新的坐标网络架构和训练方案，实现了对学习重建的频率支持的细粒度控制，并在多尺度优化的背景下解决了粗到中等估计的问题，该方法在单粒子冷冻电镜重建的实验中，取得了与最先进技术相媲美的高分辨率多尺度结构。