Search Results for author: Omar Khatib

Found 3 papers, 2 papers with code

Inverse deep learning methods and benchmarks for artificial electromagnetic material design

2 code implementations19 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.

Robust Design

Benchmarking Data-driven Surrogate Simulators for Artificial Electromagnetic Materials

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.

Benchmarking Neural Network simulation

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