Unleashing the potential: AI empowered advanced metasurface research

Y Fu, X Zhou, Y Yu, J Chen, S Wang, S Zhu… - Nanophotonics, 2024 - degruyter.com
In recent years, metasurface, as a representative of micro-and nano-optics, have
demonstrated a powerful ability to manipulate light, which can modulate a variety of physical …

Flexible inverse design of microwave filter customized on demand with wavelet transform deep learning

K Xu, J Wang, J Cai, X Ma, Q Lv… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Artificial intelligence (AI) techniques are increasingly being used for the inverse design of
microwave devices. However, several challenges, including intensive computation costs for …

Predicting the Maximum Achievable Antenna Bandwidth and Efficiency Using Machine Learning: a Terminal-Integrated Meander IFA Case Study

J Roqui, A Pegatoquet, L Santamaria… - IEEE Open Journal of …, 2024 - ieeexplore.ieee.org
In this paper, an approach based on Machine Learning (ML) to predict the maximum
achievable performance (fractional bandwidth and total efficiency) of a printed Inverted F …

[HTML][HTML] Multichannel Wavelet Kernel Network for High Dimensional Inverse Modeling of Microwave Filters

D Zhang, M Zhou, Z Wang, H Chen - Electronics, 2024 - mdpi.com
This paper proposes a multichannel wavelet kernel network (MWKN) modeling technique
with a two-stage training technique for high-dimensional inverse modeling of microwave …