IEEE AP-S/URSI 2021
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Session TH-SP.2A
Paper TH-SP.2A.9
TH-SP.2A.9
A Tailored Semi-Physics-Driven And Semi-Data-Driven Artificial Neural Network For Electromagnetic Full-Wave Inversion
Feng Han, Yanjin Chen, Xiamen University, China
Session:
Artificial Intelligence and Deep Learning: A New Era in Imaging and Inverse Scattering
Track:
Special Sessions (AP-S)
Location:
Peony Junior Ballroom 4512
Presentation Time:
Thu, 9 Dec, 11:20 - 11:40 Singapore Time (UTC +8)
Thu, 9 Dec, 04:20 - 04:40 France Time (UTC +1)
Thu, 9 Dec, 03:20 - 03:40 UTC
Wed, 8 Dec, 22:20 - 22:40 New York Time (UTC -5)
Session Co-Chairs:
Ji Chen, University of Houston and Marco Salucci, ELEDIA@UniTN - University of Trento
Session Manager:
Room R10 Manager
Presentation
Discussion
Resources
Session TH-SP.2A
TH-SP.2A.1: Deep Learning Enhanced Joint Inversion of Multiphysics Data with Nonconforming Discretization
Yanyan Hu, Jiefu Chen, Xuqing Wu, University of Houston, United States; Yueqin Huang, Cyentech Consulting LLC, United States
TH-SP.2A.2: Convolutional Neural Networks for Radio Source Detection
Jayakrishnan Vijayamohanan, Arjun Gupta, Oameed Noakoasteen,, Christos Christodoulou, University of New Mexico, United States
TH-SP.2A.3: Predicting MRI RF Exposure for Passive Implantable Medical Devices Using a Mesh-based Convolutional Neural Network
Qianlong Lan, Jianfeng Zheng, Jiajun Chang, Ran Guo, Ji Chen, University of Houston, United States; Wolfgang Kainz, US Food and Drug Administration, United States
TH-SP.2A.4: Prediction of Active Implantable Medical Device Electromagnetic Models Using a Neural Network
Jiajun Chang, Qianlong Lan, Ran Guo, Jianfeng Zheng, Ji Chen, University of Houston, United States; Wolfgang Kainz, US Food and Drug Administration, United States
TH-SP.2A.5: Deep Surrogate Models for Time-Domain Electromagnetic Analysis using Attention: Going Beyond Recurrent Neural Networks
Oameed Noakoasteen, Jayakrishnan Vijayamohanan, Arjun Gupta, Christos Christodoulou, University of New Mexico, United States
TH-SP.2A.6: Data-Driven Electromagnetic Scalar Field Estimation of a Patch Antenna Using Deep Convolutional Neural Network
Md Rayhan Khan, Constantinos L. Zekios, Shubhendu Bhardwaj, Stavros V. Georgakopoulos, Florida International University, United States
TH-SP.2A.7: Sequential Deep Learning for In-Home Activity Monitoring Using mm-Wave FMCW Radar
Hajar Abedi, Ahmad Ansariyan, Plinio Morita, Jennifer Boger, Alexander Wong, George Shaker, University of Waterloo, Canada
TH-SP.2A.8: A Machine Learning-Based Model for Fast Recognition of Orbital Angular Momentum Modes
Jia-Jing Sun, Sheng Sun, Jun Hu, University of Electronic Science and Technology of China, China
TH-SP.2A.9: A Tailored Semi-Physics-Driven And Semi-Data-Driven Artificial Neural Network For Electromagnetic Full-Wave Inversion
Feng Han, Yanjin Chen, Xiamen University, China
TH-SP.2A.10: Electromagnetic Inverse Scattering Based on Deep Learning
Renzhou Gui, Tianyu Tang, Juan Li, Huilin Zheng, Xiaohong Ji, Jun Zhao, Xiaomeng Zhao, Tongji University, China
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