Pith. sign in

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

A citation records a reference. It does not transfer a finding from one paper to another.

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

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

47 of 47 outbound references displayed

  • verified exact1
  • verified fuzzy40
  • unresolved6
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

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

Resolution
unresolved
raw_fallback, observed 2026-08-10T19:35:55.572167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.138171Z digest=sha256:7275b677c8dc46bd261cbed3be84fbcf89264782ee0e5cad081cb835f343fb16

Observation 92af11a8-2794-4991-b55c-f0651b4c9cb7 · 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 2

Resolution
unresolved
no resolver link, observed 2026-08-10T19:35:54.143890Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:35:54.143890Z digest=sha256:ddf0564ef8fca997308ca69c2f068aa23f1602c876808539e89218abec4dc6da

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:35:55.537000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.148836Z digest=sha256:7b8e30f7626f2f4cd3f4641a67e983431e8ab7ba20a1bde87e1d49e6501515e1

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:35:55.515263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.153415Z digest=sha256:afa951e119368f0650feaeacf3ead68c0d4d00b26ab7a5d7036bd634a1aba997

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:35:55.496277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.157837Z digest=sha256:aa722b9ab2d34c4ab4e9fbd05406deb27225ccc198ab0f1f2c8bff64076cc2f0

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:35:55.477445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.162228Z digest=sha256:df7c4e6d6a81d1d0975e62708ffaf87e3f6a6b0cd20e305f304a9f80a0e96c37

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:35:55.458808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.170884Z digest=sha256:0d3485207aae876f6795d295e592d2963aecefba222f4770df193fe5c0706ab0

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

Resolution
unresolved
raw_fallback, observed 2026-08-10T19:35:55.441740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.178273Z digest=sha256:faa7a194db8ba588586e79eaf8aa7202ddcf5faee728071b6e260607d97f68de

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:35:55.425026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.191217Z digest=sha256:dd1398d81a8602fb6d68d671dc4eb19516c844a4f8d3d8642fd79a70e51c837e

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:35:55.402123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.196391Z digest=sha256:bc7fb99ef228cdc8a4562a7bd75a066f764f6607bf0d4284485b1cba863ac744

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:35:55.382743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.201572Z digest=sha256:6d187921b5ffbb5d00d0b0e1b1f5c385f3846c72b6a2f8e83a33597fd155ed69

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:35:55.364443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.206545Z digest=sha256:9671935cfebf62289af8c66cb400c1e152775b58968bf6fee022e636341d8a9a

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:35:55.346795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.212370Z digest=sha256:8544c43c39a6e8a76ea932a0ac7937afc0190da4b29129cb4c0187c1443b145a

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:35:55.320622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.218259Z digest=sha256:e90f78baa2465be76623cb7de12fa50221341db869e0d7b335ede849e52cf90e

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:35:55.287950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.223345Z digest=sha256:d5e50df0caf98fcf17ba848ea11b4d41057899511d4f8fad6984af158aef73e2

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:35:55.265763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.228815Z digest=sha256:fa35193788ef0cb7c1414a06a0848771652def766cc97927e5db17dc9f346ce9

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:35:55.243388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.233622Z digest=sha256:300470219a4f51168a47b0fb9d87261fd2e29d8d120e7a89da548cf47353c109

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:35:55.223053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.238666Z digest=sha256:db16f39e4a252711cf4969490c24fea24175d24a1464beda4f0656933edebec5

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:35:55.193416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.243842Z digest=sha256:6e23fe84e2de007c18662fe85b3a565670146444e2f3db4307ad811358220add

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
raw_fallback, observed 2026-08-10T19:35:55.163173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.248741Z digest=sha256:56ae7f06877358683373271c4717969bdeb4cb5da71108d6bdce6e77d9287812

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:35:55.135802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.253842Z digest=sha256:4eb24702ed172af52b9fad0d546bd76aa5026b0ae30fc8d74593abf794b916aa

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:35:55.101749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.259297Z digest=sha256:66c78c9e3ed01d6f1acc9a14c1a890576f19d08482c29e542e4a881a26da9c26

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:35:55.077795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.264835Z digest=sha256:c54c02df18db7f1f2955dc6d450c09896ae0d9c979379673161a1d930a5999a7

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

Resolution
verified exact
local_arxiv, observed 2026-08-10T19:35:54.533556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.270254Z digest=sha256:1a5dbb0c50634040aca84feed3c134af036421cf3119d6b2176883ae14926cd1

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:35:55.041533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.276233Z digest=sha256:b7bb7aea33ec478ab596f0165f6c67f3bd5b4fb2206ea6ecb7085994e1503658

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
raw_fallback, observed 2026-08-10T19:35:55.010950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.282259Z digest=sha256:3be1066552dcc4ac1337322725979a84e1b4057037b4006f4c516ac0298e2afc

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
raw_fallback, observed 2026-08-10T19:35:54.987814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.288346Z digest=sha256:95c995c46d10210e4b66de75ced63710802391b3c1a209a2b58240bbfbeec8f2

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
raw_fallback, observed 2026-08-10T19:35:54.966375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.296334Z digest=sha256:d3c2835511db68e3b44ef4cf0dc1a2f2fcb742d17cf734b92469913793b001a0

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:35:54.937729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.305084Z digest=sha256:9163e876de0c01508f331965e86a653d5f245cdcd36efd27587785c408b2c403

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:35:54.911862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.311311Z digest=sha256:ff98331d73b4fdf5506a64896ad30ada10311f6997ec42177605e19620c7a601

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
raw_fallback, observed 2026-08-10T19:35:54.878440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.317740Z digest=sha256:d53551f4a701f1261ed9d2dcab4633f5c8c9c2048d88506aa19b1e18078dc962

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.324382Z digest=sha256:ea3040d2c20a3b425aea4a0f9fe02c2e8d4114ea82c87a80089cec3c4127a775

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:35:54.833097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.330040Z digest=sha256:4f8584ce36f7aa33ae68becd30c70f5a1a980c2a829a472b165b1ac5ab3f1919

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:35:54.809120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.335372Z digest=sha256:2b8789c688485f3a6d3b9267ef7671fda3e076bac7440c53e55dd77cd95e08ae

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:35:54.786583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.340607Z digest=sha256:f56d3de5e9e5058aae779b73fb1c0f7d768230d76a4ea9f8a0db17dd416d9705

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:35:54.765748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.347834Z digest=sha256:2e74ecc2ccb7aa5c6634fde99302148c88633e63564a29df9d9d2403febd4768

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:35:54.748173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.358647Z digest=sha256:c3264e38328c1f9b674a0ba1a2116b48af9d4cafac4c017fefc25c4215a90234

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
raw_fallback, observed 2026-08-10T19:35:54.728865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.366061Z digest=sha256:0295dec2ac04f4c0fd49fdce5a1db40cdecf3ce49ca560120d1fb0e552a5c964

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
raw_fallback, observed 2026-08-10T19:35:54.705235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.372582Z digest=sha256:4eb05f211c541f7effda722cbdf80e528ec131a3651dda763e5f163469aed8f0

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
raw_fallback, observed 2026-08-10T19:35:54.680280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.379935Z digest=sha256:c81885ab95dcfc2833d95273d599ed7a64da9997e9c802b49478863b2b955d73

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.388369Z digest=sha256:8338c2e4679d19c6d16ccb07457d4d136d04fce91e05511075bb874ccbb8c62e

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

Resolution
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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.397626Z digest=sha256:48b35b6146dcd3be37b28cd9cb9b522a9484fb2c8ff9ac952854972f817a9b95

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.402875Z digest=sha256:6ea8b1c989134b3f81c569afc1083e54550249f578b686cbc849c1d1db1ca50e

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T19:35:54.407818Z digest=sha256:f1c5980e0c04eda83096972d265fb49d87f6ae3cf01729be6dd3ababeba32293

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

Source-reported events for the cited work

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
unresolved
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
unresolved
no resolver link, observed 2026-08-10T19:35:54.428111Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Pith citing papers

No inbound Pith citation observations are available.