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Neural network–enabled accelerated discovery of multifunctional metamaterials for adaptive multispectral stealth applications
Materials Today Physics ( IF 10.0 ) Pub Date : 2025-03-06 , DOI: 10.1016/j.mtphys.2025.101696
Wei Chen , Yuping Duan , Da Ma , Meng Wang , Shude Gu , Jiangyong Liu , Yupeng Shi , Yang Yang

The development of advanced multispectral compatible stealth materials (CSMs) based on metamaterials faces significant challenges, including computational inefficiency, prohibitive costs, and the persistent issue of local optima in conventional design approaches. This study presents a transformative inverse design framework that revolutionizes the field by enabling rapid optimization within a quasi-infinite solution space. Departing from traditional low-dimensional design paradigms that are constrained by limited solution spaces and excessive reliance on manual intervention, our innovative approach introduces three key advancements: (1) a randomized cut-line coding methodology that generates an expansive, high-dimensional design space capable of addressing diverse stealth requirements; (2) a novel hybrid intelligence system combining genetic algorithms with neural networks for unprecedented computational efficiency and design flexibility; and (3) a multilayer architecture integrating conductive surface materials that achieves remarkable multispectral performance. The resulting CSMs, with a mere 1.24 mm thickness and 2.22 kg/m2 surface density, demonstrate exceptional capabilities, including ultrabroadband antireflection (reflectivity <0.1 across 8.9–18 GHz), dynamic multiband performance modulation (tunable within 6–18 GHz), radar cross-section reduction, and beam deflection - all programmable through customized fitness functions. Furthermore, the materials exhibit superior infrared stealth characteristics, achieving emissivity values as low as 0.3. This work establishes a new paradigm for the development of adaptive multispectral stealth materials, offering unprecedented versatility in diverse detection environments.

中文翻译:


神经网络加速发现用于自适应多光谱隐身应用的多功能超材料



基于超材料的先进多光谱兼容隐形材料 (CSM) 的开发面临重大挑战,包括计算效率低下、成本高昂以及传统设计方法中持续存在的局部最优问题。本研究提出了一个变革性的逆向设计框架,通过在准无限解空间内实现快速优化,彻底改变了该领域。与受有限解决方案空间和过度依赖人工干预限制的传统低维设计范式不同,我们的创新方法引入了三个关键进步:(1) 随机切割线编码方法,可生成一个广阔的高维设计空间,能够满足不同的隐身要求;(2) 一种将遗传算法与神经网络相结合的新型混合智能系统,可实现前所未有的计算效率和设计灵活性;(3) 集成导电表面材料的多层架构,可实现卓越的多光谱性能。由此产生的 CSM 厚度仅为 1.24 mm,表面密度为 2.22 kg/m2,展示了卓越的功能,包括超宽带增透(8.9–18 GHz 范围内的反射率 <0.1)、动态多频段性能调制(可在 6–18 GHz 范围内调谐)、雷达截面减小和波束偏转 - 所有这些都可以通过定制的健身函数进行编程。此外,这些材料还具有出色的红外隐身特性,发射率值低至 0.3。这项工作为自适应多光谱隐身材料的开发建立了新的范式,在不同的检测环境中提供了前所未有的多功能性。
更新日期:2025-03-06
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