47 citations · 142 across the 12 of their papers we have counts for
12 papers
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…
Segment anything, from space?
Simiao Ren, Francesco Luzi, Saad Lahrichi +4
Recently, the first foundation model developed specifically for image segmentation tasks was developed, termed the "Segment Anything Model" (SAM). SAM can segment objects in input…
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…
Mixture Manifold Networks: A Computationally Efficient Baseline for Inverse Modeling
Gregory P. Spell, Simiao Ren, Leslie M. Collins +1
We propose and show the efficacy of a new method to address generic inverse problems. Inverse modeling is the task whereby one seeks to determine the control parameters of a natura…
Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis
Simiao Ren, Wei Hu, Kyle Bradbury +4
High quality energy systems information is a crucial input to energy systems research, modeling, and decision-making. Unfortunately, actionable information about energy systems is…
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…
Utilizing geospatial data for assessing energy security: Mapping small solar home systems using unmanned aerial vehicles and deep learning
Simiao Ren, Jordan Malof, T. Robert Fetter +3
Solar home systems (SHS), a cost-effective solution for rural communities far from the grid in developing countries, are small solar panels and associated equipment that provides p…
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…
Neural-adjoint method for the inverse design of all-dielectric metasurfaces
Yang Deng, Simiao Ren, Kebin Fan +2
All-dielectric metasurfaces exhibit exotic electromagnetic responses, similar to those obtained with metal-based metamaterials. Research in all-dielectric metasurfaces currently us…
Direct evidence of void induced structural relaxations in colloidal glass formers
Cho-Tung Yip, Masaharu Isobe, Chor-Hoi Chan +7
Particle dynamics in supercooled liquids are often dominated by string-like motions in which lines of particles perform activated hops cooperatively. The structural features trigge…
Benchmarking deep inverse models over time, and the neural-adjoint method
Simiao Ren, Willie Padilla, Jordan Malof
We consider the task of solving generic inverse problems, where one wishes to determine the hidden parameters of a natural system that will give rise to a particular set of measure…