General deep learning framework for emissivity engineering
作者机构：School of Energy and Power EngineeringHuazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Low Dimensional Optoelectronic Materials and DevicesHubei University of Arts and ScienceXiangyang 441053 HubeiChina Hubei Longzhong LaboratoryWuhan University of Technology(Xiangyang Demonstration Zone)Xiangyang 441000 HubeiChina Department of Mechanical EngineeringThe University of Tokyo7-3-1 HongoBunkyo-kuTokyo 113-8654Japan
出 版 物：《Light(Science & Applications)》 (光（科学与应用)（英文版）)
年 卷 期：2023年第12卷第12期
页 面：2755-2767页
核心收录：
学科分类：0710[理学-生物学] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0809[工学-电子科学与技术（可授工学、理学学位）] 081104[工学-模式识别与智能系统] 08[工学] 0703[理学-化学] 0835[工学-软件工程] 0836[工学-生物工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：support by National Natural Science Foundation of China(52211540005,52076087,52161160332) Natural Science Foundation of Hubei Province(2023AFA072) the Open Project Program of Wuhan National Laboratory for Optoelectronics(2021WNLOKF004) Wuhan City Science and Technology Program(2020010601012197) Knowledge Innovation Shuguang Program.W.L.acknowledges the financial support from Key Research and Development plan of Hubei Province(2021BGE037) J.S.acknowledges the financial support from JSPS Bilateral Joint Research Projects(120227404)
主 题：framework multilayer autonomous
摘 要：Wavelength-selective thermal emitters(WS-TEs)have been frequently designed to achieve desired target emissivity spectra,as a typical emissivity engineering,for broad applications such as thermal camouflage,radiative cooling,and gas sensing,***,previous designs require prior knowledge of materials or structures for different applications and the designed WS-TEs usually vary from applications to applications in terms of materials and structures,thus lacking of a general design framework for emissivity engineering across different ***,previous designs fail to tackle the simultaneous design of both materials and structures,as they either fix materials to design structures or fix structures to select suitable ***,we employ the deep Q-learning network algorithm,a reinforcement learning method based on deep learning framework,to design multilayer *** demonstrate the general validity,three WS-TEs are designed for various applications,including thermal camouflage,radiative cooling and gas sensing,which are then fabricated and *** merits of the deep Q-learning algorithm include that it can(1)offer a general design framework for WS-TEs beyond one-dimensional multilayer structures;(2)autonomously select suitable materials from a self-built material library and(3)autonomously optimize structural parameters for the target emissivity *** present framework is demonstrated to be feasible and efficient in designing WS-TEs across different applications,and the design parameters are highly scalable in materials,structures,dimensions,and the target functions,offering a general framework for emissivity engineering and paving the way for efficient design of nonlinear optimization problems beyond thermal metamaterials.
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