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Paper Citation Record · LEDGER

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

As of 11 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2501.09923.

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pith.paper-citation-record.v1
2501.09923 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T19:35:54.428111Z

measured 47 of 47 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

47 of 47 outbound references displayed

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External citation measurements

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Outbound references

Observation 2a7a0bc0-62a1-4cd8-a2d1-0e4aec3ef115 · outbound

This paper cites an unresolved cited work.

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

Reference 1

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This paper cites an unresolved cited work.

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

Reference 2

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Observation 780dfb2f-e0a2-4f6d-bf42-893a9ace2455 · outbound

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

Reference 3

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Observation 9404840c-5716-41dc-b373-c29ebf55ed65 · outbound

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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Observation 6c5fba04-2483-4bc1-a40f-75996b3afb88 · outbound

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

Reference 5

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Observation d18b2f03-82c4-42c1-88cb-96ec1f45edef · outbound

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,

Reference 6

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 4dbfe494-144d-418b-a42d-c716c06c055a · outbound

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

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 269de9d7-5568-47fe-8b23-a27f03cbaa38 · outbound

This paper cites an unresolved cited work.

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

Reference 8

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Observation 766abc44-0aff-428f-b6b7-50653148e506 · outbound

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,

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation e76f96d7-09b9-4371-a294-91f94d9ba0ef · outbound

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

Reference 10

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation f1395bb0-770d-46d9-b2ed-484a82e83ed9 · outbound

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,

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation dbe55bec-bb81-4f9f-819a-97168aebbc40 · outbound

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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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation f14d41a1-38eb-4fe9-b107-0d01611b474a · outbound

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,

Reference 13

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Observation d67591ae-46f7-44dc-b5ff-de5289356024 · outbound

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,

Reference 14

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 0b41228e-d03f-40e0-bd2e-d50e32a8d84f · outbound

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,

Reference 15

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 58a13279-9f77-411b-8157-db5ecc489d2b · outbound

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,

Reference 16

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verified fuzzy
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Source-reported events for the cited work

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Observation 7b95d735-d124-4a2e-8672-c1fc969daa10 · outbound

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,

Reference 17

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 5f671e37-b078-40aa-bc5a-4b8a117cd12f · outbound

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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verified fuzzy
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Source-reported events for the cited work

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Observation 71f6d514-7e26-4aee-a78f-5428a8c1f694 · outbound

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,

Reference 19

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation af08e746-776d-42a0-9a30-45da83dc83f3 · outbound

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,

Reference 20

Resolution
verified fuzzy
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Source-reported events for the cited work

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Observation cd958515-f047-41c7-8ef0-8ddf0babfce9 · outbound

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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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 4cdacb74-5bb9-47f7-aedf-0425989c70b7 · outbound

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,

Reference 22

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation aaf40528-9078-4d35-b4be-1b185da6b710 · outbound

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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verified fuzzy
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Source-reported events for the cited work

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Observation f3e0625d-bd29-4d80-91ad-6b0393257682 · outbound

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

Reference 24

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation d9842128-2c23-446d-9584-9ac5541602f1 · outbound

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,

Reference 25

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 0f5e358a-988a-4cd4-ac9a-bc66d06559ab · outbound

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,

Reference 26

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 97d6dc0f-b63c-43d9-ac03-0cfd8c279226 · outbound

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,

Reference 27

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation f4e302b8-6294-4d9b-9753-cb0691ba0965 · outbound

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

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 6bec4cce-29c8-4eb6-ad53-2df1e726b884 · outbound

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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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 6b3214a7-58f3-4064-bb28-ee8013781334 · outbound

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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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation cdcc9020-5db5-4179-bccd-20cbe8478685 · outbound

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

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 7ef30aed-50e6-4ecb-b700-01efdd881db3 · outbound

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:35:54.852442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation fe39d73d-41ff-426f-853b-39e257b6cb78 · outbound

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 986cc0d2-1151-40da-90f3-4d0a892797d1 · outbound

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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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 3e4b7e7a-3290-4df3-b736-b5f67d1aed24 · outbound

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 26d11b12-7d9c-401f-b13a-c430b03b1a57 · outbound

This paper cites A Multi-branch Deep Learn- ing Architecture for Microwave-Ultrasound Breast Imaging,.

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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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 91195b74-98dd-4024-b89d-520daae9c32b · outbound

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 475bdcf9-4830-42b0-ac21-4a46715f665c · outbound

This paper cites 3DInvNet: A deep learning-based 3D ground-penetrating radar data inversion,.

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

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation fc7c75ae-a6c8-40f7-9db6-4ac0d963fc5b · outbound

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

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation a09cb1dc-7ff9-453e-8f5e-5b067104235d · outbound

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

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T19:35:54.379935Z digest=sha256:a461a1714df6c15f85e4d4f61edf28b0fa0d77cf32e006e3eb4f57c5660778ee

Observation de3fefd4-36ba-433b-b04e-779e953d640f · outbound

This paper cites Phase synthesis of beam-scanning reflectarray antenna based on deep learning technique,.

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:35:54.656567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T19:35:54.388369Z digest=sha256:1878673c447c8372017966b427120341fe13a1ea82da90a1efbdb97c1196b0c0

Observation 7a437147-5c84-4adb-aa0b-93294c247412 · outbound

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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verified fuzzy
raw_fallback, observed 2026-08-10T19:35:54.635906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T19:35:54.397626Z digest=sha256:57087ffa137e6314361a65a44bbaadb565dda4108ff8b083dccfd53eef038312

Observation bae91e9d-a1ac-453d-a6f8-24ae338af535 · outbound

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T19:35:54.402875Z digest=sha256:d92c4202f4aa4840909391d99cb0114655aefe3395478d880d7ce5454b3f1602

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:35:54.575051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T19:35:54.407818Z digest=sha256:6e93bf60c4d36447beb1afc100094efb9f951a10d9311b88504010268e8acaa7

Observation 00d18da5-0593-41f2-93e8-9a2cbd86ad8b · outbound

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

Resolution
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no resolver link, observed 2026-08-10T19:35:54.414998Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:35:54.414998Z digest=sha256:e3b81514e2b1ca1faf335427cb6a8274c410c90708b7c1caa45d7af3e2887e77

Observation 0a4cb673-0e84-4594-952e-bd22bb8e3813 · outbound

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

Reference 46

Resolution
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no resolver link, observed 2026-08-10T19:35:54.421184Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:35:54.421184Z digest=sha256:c823d8f461141d10a2349373c511b0832f93752bd43f45c095c36bd970ebe461

Observation 40f67f26-0ffa-49fb-8868-8e4dc6ce4716 · outbound

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

Resolution
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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:35:54.428111Z digest=sha256:e81a1d38e0a73a8b939fb64417c47f372333a0b6affdbec80bbbeecdf89648fb

Pith citing papers

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