TU-A6.1P.9

Efficient Uncertainty Quantification in Electromagnetic Modeling Using Physics-Informed Deep Operator Neural Networks

Shutong Qi, Costas Sarris, University of Toronto, Canada
Session:
Machine-Learning as Applied to EM - Trends, Advances, and Applications III Oral
Track:
AP-S: Antenna Applications and Emerging Technologies
Location:
Spadolini: 1-07
Session Time:
Tue, 16 Jul, 13:40 - 17:20
Presentation Time:
Tue, 16 Jul, 16:40 - 17:00
Session Co-Chairs:
Ergun Simsek, University of Maryland Baltimore County and Giuseppe Esposito, Consiglio Nazionale delle Ricerche
Presentation
Discussion
Session TU-A6.1P
TU-A6.1P.1: A Deep Learning-Based Imaging of Tree Interiors via a Standoff Radar System
Bui Q. Huy, Yee Hui Lee, Jiwei Qian, Kaixuan Cheng, Nanyang Technological University, Singapore; Daryl Lee, Mohamed Lokman Mohd Yusof, National Parks Singapore, Singapore; Abdulkadir C. Yucel, Nanyang Technological University, Singapore
TU-A6.1P.2: Radiating Source Modeling Using Electrical Infinitesimal Dipole Based on Machine Learning
Jaeyoun Park, Andong National University, Korea (South); Kyeong-Sik Min, Korea Maritime and Ocean Univerisity, Korea (South); Hyunchul Ku, Konkuk University, Korea (South); Jaeyul Choo, Andong National University, Korea (South)
TU-A6.1P.3: Efficient Neural-Network Based Solution of Integral Equations for Electromagnetic Analysis
Runwei Zhou, Dan Jiao, Purdue University, United States
TU-A6.1P.4: Predictive/Robust Microwave Sensor Using LSTM
Nazli Kazemi, University of Alberta, Canada; Mohammad Abdolrazzaghi, University of Toronto, Canada; Petr Musilek, University of Alberta, Canada; Elham Baladi, Polytechnique Montr´eal, Canada
TU-A6.1P.5: Electromagnetic Classification
Ergun Simsek, University of Maryland Baltimore County, United States
TU-A6.1P.6: Quantitative GPR Imaging via U-NET
Giuseppe Esposito, CNR-IREA, Italy
TU-A6.1P.7: Artificial Neural Network Topology for Modelling of Symmetrical Dipole Antenna
Darko Ninkovic, Dragan Olcan, University of Belgrade, School of Electrical Engineering, Serbia
TU-A6.1P.8: Multilayer Artificial Neural Network for Predicting the RIS-Assisted 3D-GBSM Channel Model Characteristics in Vehicle-to-Vehicle Environments
Asad Saleem, Shurun Tan, Zhejiang University-University of Illinois at Urbana-Champaign Institute, Zhejiang University, Haining, Zhejiang, China 314400, China
TU-A6.1P.9: Efficient Uncertainty Quantification in Electromagnetic Modeling Using Physics-Informed Deep Operator Neural Networks
Shutong Qi, Costas Sarris, University of Toronto, Canada
TU-A6.1P.10: Synthetic Electromagnetic Emissions: A New Approach to EMC Compliance Testing
Oameed Noakoasteen, Mohammad Abedi, Christos Christodoulou, University of New Mexico, United States; Sameer Hemmady, Verus Research, United States; Edl Schamiloglu, University of New Mexico, United States
Resources
View Manuscript