3 citations · 11 across the 9 of their papers we have counts for
9 papers
Active Electronic Terahertz Imaging for Industrial Applications: From Hardware to the Paradigm Shift by Artificial Intelligence
Aparajita Bandyopadhyay, Hui Yuan, Willie J. Padilla +2
Imaging with terahertz (THz) radiation (0.3-10 THz) benefits from a unique combination of attributes: penetration through dry, non-polar packaging materials; variations of dielectr…
Learning Electromagnetic Metamaterial Physics With ChatGPT
Darui Lu, Yang Deng, Jordan M. Malof +1
Large language models (LLMs) such as ChatGPT, Gemini, LlaMa, and Claude are trained on massive quantities of text parsed from the internet and have shown a remarkable ability to re…
Fundamental absorption bandwidth to thickness limit for transparent homogeneous layers
Willie J. Padilla, Yang Deng, Omar Khatib +1
Past work has considered the analytic properties of the reflection coefficient for a metal-backed slab. The primary result established a fundamental relationship for the minimal la…
Deep Generalized Green's Functions
Rixi Peng, Juncheng Dong, Jordan Malof +2
In this study, we address the challenge of obtaining a Green's function operator for linear partial differential equations (PDEs). The Green's function is well-sought after due to…
Machine Learning for Mie-Tronics
Wenhao Li, Hooman Barati Sedeh, Willie J. Padilla +3
Electromagnetic multipole expansion theory underpins nanoscale light-matter interactions, particularly within subwavelength meta-atoms, paving the way for diverse and captivating o…
Does Deep Active Learning Work in the Wild?
Simiao Ren, Saad Lahrichi, Yang Deng +3
Deep active learning (DAL) methods have shown significant improvements in sample efficiency compared to simple random sampling. While these studies are valuable, they nearly always…
Towards Robust Deep Active Learning for Scientific Computing
Simiao Ren, Yang Deng, Willie J. Padilla +1
Deep learning (DL) is revolutionizing the scientific computing community. To reduce the data gap, active learning has been identified as a promising solution for DL in the scientif…
Inverse deep learning methods and benchmarks for artificial electromagnetic material design
Simiao Ren, Ashwin Mahendra, Omar Khatib +3
Deep learning (DL) inverse techniques have increased the speed of artificial electromagnetic material (AEM) design and improved the quality of resulting devices. Many DL inverse te…
Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions
Juncheng Dong, Simiao Ren, Yang Deng +5
Numerous physical systems are described by ordinary or partial differential equations whose solutions are given by holomorphic or meromorphic functions in the complex domain. In ma…