Zobrazeno 1 - 10
of 61
pro vyhledávání: '"Ren Simiao"'
Publikováno v:
Nanophotonics, Vol 13, Iss 13, Pp 2323-2334 (2024)
We demonstrate transfer learning as a tool to improve the efficacy of training deep learning models based on residual neural networks (ResNets). Specifically, we examine its use for study of multi-scale electrically large metasurface arrays under ope
Externí odkaz:
https://doaj.org/article/bca98c5481a04b66b65d7344acb91bc2
Autor:
Li, Wenhao, Sedeh, Hooman Barati, Padilla, Willie J., Ren, Simiao, Malof, Jordan, Litchinitser, Natalia M.
Electromagnetic multipole expansion theory underpins nanoscale light-matter interactions, particularly within subwavelength meta-atoms, paving the way for diverse and captivating optical phenomena. While conventionally brute force optimization method
Externí odkaz:
http://arxiv.org/abs/2305.18589
Autor:
Ren, Simiao, Luzi, Francesco, Lahrichi, Saad, Kassaw, Kaleb, Collins, Leslie M., Bradbury, Kyle, Malof, Jordan M.
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 imagery based on cheap input prompts, such as one (or more) points, a
Externí odkaz:
http://arxiv.org/abs/2304.13000
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 assume that optimal DAL hyperparameter (HP) settings are known in adv
Externí odkaz:
http://arxiv.org/abs/2302.00098
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 natural system that produce a given set of observed measurements. Recent wor
Externí odkaz:
http://arxiv.org/abs/2211.14366
The use of synthetic (or simulated) data for training machine learning models has grown rapidly in recent years. Synthetic data can often be generated much faster and more cheaply than its real-world counterpart. One challenge of using synthetic imag
Externí odkaz:
http://arxiv.org/abs/2209.08685
Autor:
Ren, Simiao, Hu, Wei, Bradbury, Kyle, Harrison-Atlas, Dylan, Valeri, Laura Malaguzzi, Murray, Brian, Malof, Jordan M.
Publikováno v:
Applied Energy, 326, 119876 (2022)
High quality energy systems information is a crucial input to energy systems research, modeling, and decision-making. Unfortunately, actionable information about energy systems is often of limited availability, incomplete, or only accessible for a su
Externí odkaz:
http://arxiv.org/abs/2202.12939
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 scientific computing community. However, the deep active learning (DAL) litera
Externí odkaz:
http://arxiv.org/abs/2201.12632
Publikováno v:
ISPRS Int. J. Geo-Inf. 2022, 11(4), 222
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 power to a single household. A crucial resource for targeting further i
Externí odkaz:
http://arxiv.org/abs/2201.05548
Autor:
Ren, Simiao, Mahendra, Ashwin, Khatib, Omar, Deng, Yang, Padilla, Willie J., Malof, Jordan M.
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 techniques have succeeded on a number of AEM design tasks, but to compar
Externí odkaz:
http://arxiv.org/abs/2112.10254