2 code implementations • 19 Dec 2021 • Simiao Ren, Ashwin Mahendra, Omar Khatib, Yang Deng, Willie J. Padilla, Jordan M. Malof
Deep learning (DL) inverse techniques have increased the speed of artificial electromagnetic material (AEM) design and improved the quality of resulting devices.
no code implementations • ICLR 2022 • Juncheng Dong, Simiao Ren, Yang Deng, Omar Khatib, Jordan Malof, Mohammadreza Soltani, Willie Padilla, Vahid Tarokh
To this end, we propose a physics-infused deep neural network based on the Blaschke products for phase retrieval.
1 code implementation • NeurIPS 2021 • Yang Deng*, Juncheng Dong*, Simiao Ren*, Omar Khatib, Mohammadreza Soltani, Vahid Tarokh, Willie Padilla, Jordan Malof
Recently, it has been shown that deep learning can be an alternative solution to infer the relationship between an AEM geometry and its properties using a (relatively) small pool of CEMS data.