Illustration of emissivity engineering and deep learning framework. (IMAGE)
Caption
a, Schematic of three typical applications in emissivity engineering, including thermal camouflage, radiative cooling, and gas sensing. The basic elements of Deep Q-learning network (DQN)-based framework, including materials and structures for inputs and action decisions for outputs. b, Schematic of the DQN framework. The state consists of two materials and five layers of thickness of the multilayer, then the state parameters are fed into the DQN to generate an Action. Then take the action to update the state. Transfer matrix method (TMM) is adopted to simulate the new state, and reward is obtained to feed back to neural network. The new state is fed into the DQN for next iteration. Each pair of state, action and reward is recorded as dataset to train the neural network, so that it can take the action that increases accumulated reward and finally get the corresponding state with the maximum reward. c, Emissivity spectrum of the WS-TE optimized for thermal camouflage. The reward is defined as the difference between the average emissivity outside and inside the atmospheric window (8-13 μm). d, Optimization process of WS-TE for thermal camouflage. Maximum of reward as a function of the percentage of calculated structures. Only 4.428% of the structures are calculated to find the optimal structure for thermal camouflage.
Credit
by Shilv Yu, Peng Zhou, Wang Xi, Zihe Chen, Yuheng Deng, Xiaobing Luo, Wangnan Li, Junichiro Shiomi, and Run Hu
Usage Restrictions
Credit must be given to the creator.
License
CC BY