Total transmission plays an important role in efficiency improvement and wavefront control,and has made great progress in many applications,such as the optical film and signal transmission.Therefore,many traditional p...Total transmission plays an important role in efficiency improvement and wavefront control,and has made great progress in many applications,such as the optical film and signal transmission.Therefore,many traditional physical methods represented by transformation optics have been studied to achieve total transmission.However,these methods have strict limitations on the size of the photonic structure,and the calculation is complex.Here,we exploit deep learning to achieve this goal.In deep learning,the data-driven prediction and design are carried out by artificial neural networks(ANNs),which provide a convenient architecture for large dataset problems.By taking the transmission characteristic of the multi-layer stacks as an example,we demonstrate how optical materials can be designed by using ANNs.The trained network directly establishes the mapping from optical materials to transmission spectra,and enables the forward spectral prediction and inverse material design of total transmission in the given parameter space.Our work paves the way for the optical material design with special properties based on deep learning.展开更多
The propagation of acoustic waves is a fundamental topic in shallow ocean acoustics.We numerically demonstrate a three-dimensional zone of silence consisting of a circular tube with gradient index metamaterials attach...The propagation of acoustic waves is a fundamental topic in shallow ocean acoustics.We numerically demonstrate a three-dimensional zone of silence consisting of a circular tube with gradient index metamaterials attached to its rigid wall.The cloaking effect is verified by fine agreement with analytical calculations.展开更多
基金supported by the National Key Research and Development Program of China under Grant No.2020YFA0710100the National Natural Science Foundation of China under Grants No.92050102,No.11874311,and No.11504306the Fundamental Research Funds for the Central Universities under Grant No.20720200074。
文摘Total transmission plays an important role in efficiency improvement and wavefront control,and has made great progress in many applications,such as the optical film and signal transmission.Therefore,many traditional physical methods represented by transformation optics have been studied to achieve total transmission.However,these methods have strict limitations on the size of the photonic structure,and the calculation is complex.Here,we exploit deep learning to achieve this goal.In deep learning,the data-driven prediction and design are carried out by artificial neural networks(ANNs),which provide a convenient architecture for large dataset problems.By taking the transmission characteristic of the multi-layer stacks as an example,we demonstrate how optical materials can be designed by using ANNs.The trained network directly establishes the mapping from optical materials to transmission spectra,and enables the forward spectral prediction and inverse material design of total transmission in the given parameter space.Our work paves the way for the optical material design with special properties based on deep learning.
基金National Natural Science Foundation of China(Grant No.11874311)Natural Science Foundation of Fujian Province,China(Grant No.2017J05015)。
文摘The propagation of acoustic waves is a fundamental topic in shallow ocean acoustics.We numerically demonstrate a three-dimensional zone of silence consisting of a circular tube with gradient index metamaterials attached to its rigid wall.The cloaking effect is verified by fine agreement with analytical calculations.
