Paper Citation Record · LEDGER

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming

As of 9 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2507.11547.

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

pith.paper-citation-record.v1
2507.11547 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:44:50.613559Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

40 of 40 outbound references displayed

verified exact13
verified fuzzy8
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch4

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3b61acd9-3008-4b26-9ed4-eff99df382cb · outbound

This paper cites an unresolved cited work.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:44:55.789932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6a6b6f15-5900-403b-aa24-8f2c9ac9b314 · outbound

This paper cites A new design guideline development strategy for aluminium alloy corners formed through cold and hot stamping processes,.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming A new design guideline development strategy for aluminium alloy corners formed through cold and hot stamping processes,

Reference 2

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T18:44:54.211299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fa6f28b2-5b33-44f0-a2d3-f2b7c18889c2 · outbound

This paper cites Optimization of an aluminum alloy anti -collision side beam hot stamping process using a multi -objective genetic algorithm,.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming Optimization of an aluminum alloy anti -collision side beam hot stamping process using a multi -objective genetic algorithm,

Reference 3

Resolution: verified exact; doi, observed 2026-08-06T18:44:52.374110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a3593a26-9509-4ad8-911e-43858bd094b6 · outbound

This paper cites A study on the buckling behaviour of aluminium alloy sheet in deep drawing with macro -textured blankholder,.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming A study on the buckling behaviour of aluminium alloy sheet in deep drawing with macro -textured blankholder,

Reference 4

Resolution: verified exact; doi, observed 2026-08-06T18:44:52.245810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bde0181a-9e0c-45a5-adaa-b07673aaf39b · outbound

This paper cites Rapid feasibility assessment of components to be formed through hot stamping: A deep learning approach,.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming Rapid feasibility assessment of components to be formed through hot stamping: A deep learning approach,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T18:44:48.192641Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7412d0fe-04fb-4cbf-aef9-9d96dfe9eee6 · outbound

This paper cites A Study on Using Image -Based Machine Learning Methods to Develop Surrogate Models of Stamp Forming Simulations,.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming A Study on Using Image -Based Machine Learning Methods to Develop Surrogate Models of Stamp Forming Simulations,

Reference 6

Resolution: verified exact; doi, observed 2026-08-06T18:44:52.054746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 94c504e4-1d3f-49a4-a1a4-36b9fe2e608b · outbound

This paper cites Springback prediction for sheet metal cold stamping using convolutional neural networks,.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming Springback prediction for sheet metal cold stamping using convolutional neural networks,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:44:55.657944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 26b93513-6418-48b0-9276-d253db10a242 · outbound

This paper cites Deep residual U-net with input of static structural responses for efficient U* load transfer path analysis,.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming Deep residual U-net with input of static structural responses for efficient U* load transfer path analysis,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T18:44:48.410841Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16aee80a-21c8-49c0-8635-233aae1a1ba6 · outbound

This paper cites FuS -GCN: Efficient B -rep based graph convolutional networks for 3D -CAD model classification and retrieval,.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming FuS -GCN: Efficient B -rep based graph convolutional networks for 3D -CAD model classification and retrieval,

Reference 9

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T18:44:53.923095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e353de0d-3a22-4a64-ba38-b46d9e414b1e · outbound

This paper cites Graph neural networks: A review of methods and applications,.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming Graph neural networks: A review of methods and applications,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T18:44:48.506413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e4ccf8e-a120-419d-8610-c19722e9a449 · outbound

This paper cites Learning Mesh-Based Simulation with Graph Networks.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming Learning Mesh-Based Simulation with Graph Networks

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T18:44:48.588332Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7f1fbb3-35ed-4a5d-a0e0-c7fa1d6a3ecc · outbound

This paper cites Everything is connected: Graph neural networks,.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming Everything is connected: Graph neural networks,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T18:44:48.645195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aabafef3-6df1-4e20-bfce-b4e4096f6760 · outbound

This paper cites Learning to simulate complex physics with graph networks,.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming Learning to simulate complex physics with graph networks,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:44:55.305643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9340787f-aa31-4cae-a5fb-00074e6f1867 · outbound

This paper cites Relational inductive biases, deep learning, and graph networks.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming Relational inductive biases, deep learning, and graph networks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T18:44:48.797236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2928c53-6820-4aa8-97c5-7b498b5baf5c · outbound

This paper cites A review of graph neural network applications in mechanics -related domains,.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming A review of graph neural network applications in mechanics -related domains,

Reference 15

Resolution: verified exact; doi, observed 2026-08-06T18:44:51.801155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d841aa34-d203-45d2-8c26-b06cd53759fe · outbound

This paper cites Graph neural network -assisted evolutionary algorithm for rapid optimization design of shear-wall structures,.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming Graph neural network -assisted evolutionary algorithm for rapid optimization design of shear-wall structures,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T18:44:48.895850Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a52c65e9-a3ad-4bb4-86c0-cc8f72a81048 · outbound

This paper cites Design -condition-informed shear wall layout design based on graph neural networks,.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming Design -condition-informed shear wall layout design based on graph neural networks,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T18:44:48.944346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8bb7d11-ef8f-4dc9-95b5-3d6a662d11f2 · outbound

This paper cites Learning finite element convergence with the Multi-fidelity Graph Neural Network,.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming Learning finite element convergence with the Multi-fidelity Graph Neural Network,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:44:55.126426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation aba9b429-6df1-46f4-b115-1e50d85b4085 · outbound

This paper cites Learning rigid dynamics with face interaction graph networks.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming Learning rigid dynamics with face interaction graph networks

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T18:44:49.091650Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aaebdd3c-e08b-4255-bc22-331fb4fa832e · outbound

This paper cites Physics-Encoded Graph Neural Networks for Deformation Prediction under Contact.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming Physics-Encoded Graph Neural Networks for Deformation Prediction under Contact

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-06T18:44:53.359007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 65ec931a-5225-478a-a198-439d835bff40 · outbound

This paper cites Graph networks as learnable physics engines for inference and control,.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming Graph networks as learnable physics engines for inference and control,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:44:54.943545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7fce4633-f3ef-46ea-943e-09c46657f6cf · outbound

This paper cites Towards Fast Simulation of Environmental Fluid Mechanics with Multi-Scale Graph Neural Networks.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming Towards Fast Simulation of Environmental Fluid Mechanics with Multi-Scale Graph Neural Networks

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T18:44:49.316212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82885fa8-17c4-4f32-8f6b-c4b8c26dc6ac · outbound

This paper cites Predicting dynamic responses of continuous deformable bodies:A graph-based learning approach,.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming Predicting dynamic responses of continuous deformable bodies:A graph-based learning approach,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T18:44:49.386845Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4222db99-0afe-420d-bb53-99053c49e1d3 · outbound

This paper cites Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems,.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems,

Reference 24

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T18:44:53.117693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 48d37585-241f-49fb-ac4e-87dae9d350fe · outbound

This paper cites Physics -informed graph neural network emulation of soft-tissue mechanics,.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming Physics -informed graph neural network emulation of soft-tissue mechanics,

Reference 25

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T18:44:52.975993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2cef52d8-92e9-4888-b864-4f19c294c979 · outbound

This paper cites On the use of graph neural networks and shape - function-based gradient computation in the deep energy method,.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming On the use of graph neural networks and shape - function-based gradient computation in the deep energy method,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:44:54.792225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 231054a9-e21e-47ea-82b7-995a9f66e55d · outbound

This paper cites Metal forming progress since 2000,.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming Metal forming progress since 2000,

Reference 27

Resolution: verified exact; doi, observed 2026-08-06T18:44:51.637559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b3be8575-26d5-47a2-bafd-b35d075e7103 · outbound

This paper cites Numerical analysis on the elastic deformation of the tools in sheet metal forming processes,.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming Numerical analysis on the elastic deformation of the tools in sheet metal forming processes,

Reference 28

Resolution: verified exact; doi, observed 2026-08-06T18:44:51.430616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 26016d0a-7fc6-4f32-8815-69d233a589fa · outbound

This paper cites Experimental investigation of boron steel at hot stamping conditions,.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming Experimental investigation of boron steel at hot stamping conditions,

Reference 29

Resolution: verified exact; doi, observed 2026-08-06T18:44:51.220605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 49a9375d-44a0-4d70-b88c-206a23d16c78 · outbound

This paper cites Investigation of deformation and failure features in hot stamping of AA6082: Experimentation and modelling,.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming Investigation of deformation and failure features in hot stamping of AA6082: Experimentation and modelling,

Reference 30

Resolution: verified exact; doi, observed 2026-08-06T18:44:51.073948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 39900be6-9bf9-4e98-9fd2-9940a8589ccb · outbound

This paper cites MAgNET: A graph U -Net architecture for mesh-based simulations,.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming MAgNET: A graph U -Net architecture for mesh-based simulations,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T18:44:49.931160Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3635eba-d220-42ce-a62e-8e0c9770bf9d · outbound

This paper cites MultiScale MeshGraphNets.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming MultiScale MeshGraphNets

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T18:44:50.001723Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81aabf26-9a34-4e6b-99f1-9c682d3a8536 · outbound

This paper cites Learning rigid-body simulators over implicit shapes for large-scale scenes and vision.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming Learning rigid-body simulators over implicit shapes for large-scale scenes and vision

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-08-06T18:44:52.707789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 680cbcba-d67a-43a3-a7f1-76a85c1ad7a0 · outbound

This paper cites ST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modeling.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming ST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modeling

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T18:44:50.152301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d8b8fb6-935a-4bb2-b9e6-281ffa4e1fa1 · outbound

This paper cites An integrated preforming -performance model for high - fidelity performance analysis of cured woven composite part with non -orthogonal yarn angles,.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming An integrated preforming -performance model for high - fidelity performance analysis of cured woven composite part with non -orthogonal yarn angles,

Reference 35

Resolution: verified exact; doi, observed 2026-08-06T18:44:50.943370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 988179fc-644b-4451-9889-995ec639f1e2 · outbound

This paper cites An investigation of a new 2D CDM model in predicting failure in HFQing of an automotive panel,.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming An investigation of a new 2D CDM model in predicting failure in HFQing of an automotive panel,

Reference 36

Resolution: verified exact; raw_fallback, observed 2026-08-06T18:44:52.589335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0013f0a7-ad14-4fbd-a588-30774de0599a · outbound

This paper cites An investigation of lubrication and heat transfer for a sheet aluminium heat, form-quench (HFQ) process,.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming An investigation of lubrication and heat transfer for a sheet aluminium heat, form-quench (HFQ) process,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:44:54.646986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 657d2cc8-7c8c-407d-a675-50fe25d4ae06 · outbound

This paper cites Sliding interfaces with contact-impact in large-scale Lagrangian computations,.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming Sliding interfaces with contact-impact in large-scale Lagrangian computations,

Reference 38

Resolution: verified exact; doi, observed 2026-08-06T18:44:50.817797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 69bb0672-37e7-4e21-881a-2272ff88babe · outbound

This paper cites Neural message passing for quantum chemistry,.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming Neural message passing for quantum chemistry,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:44:54.496212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation feba9993-83c7-4d27-aac4-b8a1f72eeefe · outbound

This paper cites Efficient learning of mesh -based physical simulation with bi -stride multi -scale graph neural network,.

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming Efficient learning of mesh -based physical simulation with bi -stride multi -scale graph neural network,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:44:54.357070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

No inbound Pith citation observations are available.