Inverse Design of Folded Waveguide SWSs for Application in TWTs Based on Transfer Learning of Deep Neural Network
IEEE Transactions on Plasma Science(2022)
摘要
This article reports on the design and demonstration of a practical bidirectional fully connected deep neural network (BFC-DNN) for folded waveguide (FWG) slow wave structures (SWSs) in multiple frequency bands, which can be used to speed up the design process of FWG-SWS traveling-wave tube (TWT) with high performance in different frequency bands. The BFC-DNN is first trained to inverse design FWG-SWS using exact numerical simulation results in a form of supervised learning, which shows that the training loss is lower than 0.05. The simulation results of CST demonstrate that the transmission of the structure designed by the BFC-DNN is higher than −0.1 dB at 34 GHz, with a phase velocity of 0.267 c. Based on the transfer learning, the pretrained BFC-DNN model can be fine-tuned from the
$K\!a$
-band with a smaller dataset at 850 GHz, and the inverse design of an 850-GHz central frequency FWG-SWS can be successfully generated. The simulation results show that the output power of the structure designed by the BFC-DNN is 2.86 W and the gain reaches 33.6 dB, where the input power is 1.25 mW at 850 GHz.
更多查看译文
关键词
Deep neural network (DNN),folded waveguide (FWG),inverse design,slow wave structure (SWS),traveling-wave tube (TWT)
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要