斗篷
掩蔽
超材料
各向同性
计算机科学
隐身
热的
人工神经网络
人工智能
声学
光学
物理
气象学
作者
Qingxiang Ji,Yunchao Qi,Chenwei Liu,Sheng Meng,Jun Liang,Muamer Kadic,Guodong Fang
标识
DOI:10.1016/j.ijheatmasstransfer.2022.122716
摘要
Thermal manipulation has been widely researched due to its potentials in novel functions, such as cloaking, illusion and sensing. However, thermal manipulation is often realized by metamaterials which entails extreme material properties. Here, we propose a machine learning based thermal cloak consisting of a finite number of layers with isotropic materials. An artificial neural network is established to intelligently learn the relation between each layer’s constitutive properties and the cloaking performances. Optimal material properties are retrieved so that heat flows can be directed to detour the cloaked object without any invasion, as if the object is not there. The designed cloak demonstrates both easiness to implement in applications and excellent performances in thermal invisibility, which are verified by simulations and experiments. The proposed method can be flexibly extended to other physical fields, like acoustics and electromagnetics, providing inspiration for metamaterials design in a wide range of communities.
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