WE-A6.2A.9
Prediction of the Electrostatic Polarizability of 3D Lunar Regolith Particles using a Data-Driven 3D Deep Learning Approach
Kameswara Mantha, Somen Baidya, University of Missouri-Kansas City, United States; Edward J Garboczi, National Institute of Standards and Technology, United States; Ahmed M Hassan, University of Missouri-Kansas City, United States
Session:
Neural Networks in Electromagnetic Field Computations Oral
Track:
AP-S: Track 6: Computational Electromagnetics
Location:
Room 251B
Session Time:
Wed, 15 Jul, 08:00 - 11:40
Presentation Time:
Wed, 15 Jul, 11:00 - 11:20
Session Chair:
Costas Sarris, University of Toronto
Presentation
Discussion
Session WE-A6.2A
WE-A6.2A.1: Physics-Informed Neural Networks for the Time-Domain Maxwell Equations with Split-Field Perfectly Matched Layers
xiaodong Liu, Remcom Inc, United States; Lingquan Li, Shanghai University, China; Gregory Moss, Scott Langdon, Remcom Inc, United States
WE-A6.2A.2: A Neural Network-based Solver for Closed and Open Electromagnetic Structures
Nusrat Zahan Priota, John Volakis, Constantinos Zekios, Florida International University, United States
WE-A6.2A.3: Advanced Physics-Informed Deep Operator Network for Efficient Uncertainty Analysis and Optimization of Periodic Structures
Shutong Qi, Costas Sarris, University of Toronto, Canada
WE-A6.2A.4: Adaptive Multi-Grid Graph Element Networks for PDE Solutions on Irregular Finite Element Meshes
Nayem Hosen, Meratun Anee, Su Yan, Howard University, United States
WE-A6.2A.5: Time-Evolving Natural Gradient Extended to the Wave Equation
Bertram Thomas, U.S. Naval Research Laboratory, United States
WE-A6.2A.6: Physics-Informed Fourier Neural Operators for Microwave Diffraction Tomography Simulations
Léo Monnier, Alexandre Baussard, Université Technologique de Troyes, France; Cyrille-Jean Enderli, Guillaume Reille, THALES DMS, France
WE-A6.2A.7: Device Diagnosis with the Use of Neural Networks
Nusrat Zahan Priota, John Volakis, Constantinos Zekios, Florida International University, United States
WE-A6.2A.8: Stack Selection for Multilayer Huygens’ Meta-Atoms and Accurate Inverse Design with a Hybrid Semianalytical and Deep Learning Framework
Natanel Nissan, Tel Aviv University, Israel; Sherman Marcus, Technion - Israel Institute of Technology, Israel; Dan Raviv, Raja Giryes, Tel Aviv University, Israel; Ariel Epstein, Technion - Israel Institute of Technology, Israel
WE-A6.2A.9: Prediction of the Electrostatic Polarizability of 3D Lunar Regolith Particles using a Data-Driven 3D Deep Learning Approach
Kameswara Mantha, Somen Baidya, University of Missouri-Kansas City, United States; Edward J Garboczi, National Institute of Standards and Technology, United States; Ahmed M Hassan, University of Missouri-Kansas City, United States
WE-A6.2A.10: Machine Learning-Driven Geometry Prediction of Metasurface-Enabled Antennas for 5G Energy Harvesting
Taimoor Khan, Hrisikesh Roy, Binod Kumar Kanaujia, National Institute of Technology Silchar, India
Resources
No resources available.