Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks | Paper Citation Record

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Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Unresolved cited work

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This paper cites Jin, Theory and computation of electromagnetic fields.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Jin, Theory and computation of electromagnetic fields

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This paper cites Chew, M.-S.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Chew, M.-S

Reference 4

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This paper cites Pastorino and A.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Pastorino and A

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This paper cites 2.5 D forward and inverse modeling for interpreting low- frequency electromagnetic measurements: Geophysics, 73,.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks 2.5 D forward and inverse modeling for interpreting low- frequency electromagnetic measurements: Geophysics, 73,

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This paper cites Jin, Electromagnetic scattering modelling for quantitative remote sensing.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Jin, Electromagnetic scattering modelling for quantitative remote sensing

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This paper cites Computational elec- tromagnetics: the finite-difference time-domain method,.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Computational elec- tromagnetics: the finite-difference time-domain method,

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This paper cites Jin, The finite element method in electromagnetics.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Jin, The finite element method in electromagnetics

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This paper cites Discontinuous galerkin time-domain methods for multiscale electromagnetic simulations: A review,.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Discontinuous galerkin time-domain methods for multiscale electromagnetic simulations: A review,

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This paper cites The adaptive cross approxima- tion algorithm for accelerated method of moments computations of emc problems,.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks The adaptive cross approxima- tion algorithm for accelerated method of moments computations of emc problems,

Reference 12

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This paper cites Application of fft and the conjugate gradient method for the solution of electromagnetic radiation from electrically large and small conducting bodies,.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Application of fft and the conjugate gradient method for the solution of electromagnetic radiation from electrically large and small conducting bodies,

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This paper cites Rapid solution of integral equations of classical potential theory,.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Rapid solution of integral equations of classical potential theory,

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This paper cites Machine learning in electromagnetics: A review and some perspectives for future research,.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Machine learning in electromagnetics: A review and some perspectives for future research,

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This paper cites DNNs as applied to electromagnetics, antennas, and propagation—A review,.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks DNNs as applied to electromagnetics, antennas, and propagation—A review,

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This paper cites Artificial Intelligence: New Frontiers in Real–Time Inverse Scattering and Electromagnetic Imaging,.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Artificial Intelligence: New Frontiers in Real–Time Inverse Scattering and Electromagnetic Imaging,

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This paper cites A review of deep learning approaches for inverse scattering problems (invited review),.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks A review of deep learning approaches for inverse scattering problems (invited review),

Reference 18

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This paper cites Machine-learning-based PML for the FDTD method,.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Machine-learning-based PML for the FDTD method,

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This paper cites Study on a fast solver for Poisson’s equation based on deep learning technique,.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Study on a fast solver for Poisson’s equation based on deep learning technique,

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This paper cites Application of Multitask Learning for 2-D Modeling of Magnetotelluric Surveys: TE Case,.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Application of Multitask Learning for 2-D Modeling of Magnetotelluric Surveys: TE Case,

Reference 21

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This paper cites Machine-Learning- Based Hybrid Method for the Multilevel Fast Multipole Algorithm,.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Machine-Learning- Based Hybrid Method for the Multilevel Fast Multipole Algorithm,

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This paper cites A Surrogate Model for the Rapid Evaluation of Electromagnetic-Thermal Effects under Humid Air Conditions,.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks A Surrogate Model for the Rapid Evaluation of Electromagnetic-Thermal Effects under Humid Air Conditions,

Reference 23

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This paper cites RayProNet: A Neural Point Field Framework for Radio Propagation Modeling in 3D Environments.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks RayProNet: A Neural Point Field Framework for Radio Propagation Modeling in 3D Environments

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This paper cites Multi-Frequency Data Acquisition Model and Hybrid Neu- ral Network for Precise Electromagnetic Wellbore Casing Inspection,.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Multi-Frequency Data Acquisition Model and Hybrid Neu- ral Network for Precise Electromagnetic Wellbore Casing Inspection,

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This paper cites Learning-based fast electromagnetic scattering solver through generative adversarial network,.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Learning-based fast electromagnetic scattering solver through generative adversarial network,

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This paper cites Predicting macro basis functions for method of moments scattering problems using deep neural networks,.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Predicting macro basis functions for method of moments scattering problems using deep neural networks,

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This paper cites An AI Predictor: From Point Clouds to Scattered Far Fields for 3-D PEC Targets,.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks An AI Predictor: From Point Clouds to Scattered Far Fields for 3-D PEC Targets,

Reference 28

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This paper cites Hybrid Physics-Informed Neural Network for the Wave Equation with Unconditionally Stable Time-Stepping,.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Hybrid Physics-Informed Neural Network for the Wave Equation with Unconditionally Stable Time-Stepping,

Reference 29

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This paper cites Electromagnetic Modeling Using an FDTD-Equivalent Recurrent Convolution Neural Network: Accurate Computing on a Deep Learning Framework.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Electromagnetic Modeling Using an FDTD-Equivalent Recurrent Convolution Neural Network: Accurate Computing on a Deep Learning Framework

Reference 30

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This paper cites A theory-guided deep neural network for time domain electromagnetic simulation and inversion using a differentiable programming platform,.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks A theory-guided deep neural network for time domain electromagnetic simulation and inversion using a differentiable programming platform,

Reference 31

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This paper cites Physics- informed supervised residual learning for electromagnetic modeling,.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Physics- informed supervised residual learning for electromagnetic modeling,

Reference 32

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This paper cites Solving Combined Field Integral Equations with Physics-informed Graph Residual Learning for EM Scattering of 3D PEC Targets,.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Solving Combined Field Integral Equations with Physics-informed Graph Residual Learning for EM Scattering of 3D PEC Targets,

Reference 33

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This paper cites DeepNIS: Deep neural network for nonlinear electromagnetic inverse scattering,.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks DeepNIS: Deep neural network for nonlinear electromagnetic inverse scattering,

Reference 34

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This paper cites Electromagnetic inverse scattering with perceptual generative adversarial networks,.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Electromagnetic inverse scattering with perceptual generative adversarial networks,

Reference 35

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Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks A Multi-branch Deep Learn- ing Architecture for Microwave-Ultrasound Breast Imaging,

Reference 36

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This paper cites Physics-Informed Supervised Residual Learning for 2-D Inverse Scattering Problems,.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Physics-Informed Supervised Residual Learning for 2-D Inverse Scattering Problems,

Reference 37

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Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks 3DInvNet: A deep learning-based 3D ground-penetrating radar data inversion,

Reference 38

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This paper cites Neural born iterative method for solving inverse scattering problems: 2D cases,.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Neural born iterative method for solving inverse scattering problems: 2D cases,

Reference 39

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This paper cites Unrolled convo- lutional neural network for full-wave inverse scattering,.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Unrolled convo- lutional neural network for full-wave inverse scattering,

Reference 40

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Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Phase synthesis of beam-scanning reflectarray antenna based on deep learning technique,

Reference 41

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This paper cites Coding programmable metasurfaces based on deep learning techniques,.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Coding programmable metasurfaces based on deep learning techniques,

Reference 42

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This paper cites Real-Time Precision Prediction of 3-D Package Thermal Maps via Image-to-Image Translation,.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Real-Time Precision Prediction of 3-D Package Thermal Maps via Image-to-Image Translation,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:35:54.609752Z

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Observation 7d5786da-dfaa-4e2e-b355-12104903efbd · outbound

This paper cites Artificial Neural Networks for Microwave Computer-Aided Design: The State of the Art,.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Artificial Neural Networks for Microwave Computer-Aided Design: The State of the Art,

Reference 44

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This paper cites Fourier Neural Operator for Parametric Partial Differential Equations.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Fourier Neural Operator for Parametric Partial Differential Equations

Reference 45

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This paper cites Neural Message Passing for Quantum Chemistry.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks Neural Message Passing for Quantum Chemistry

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This paper cites A comprehensive survey on transfer learning,.

Study on a Fast Solver for Combined Field Integral Equations of 3D Conducting Bodies Based on Graph Neural Networks A comprehensive survey on transfer learning,

Reference 47

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