3 citations · 8 across the 7 of their papers we have counts for
4 papers · 1 filter
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…