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Paper Citation Record · LEDGER

Learning to Simulate Complex Physics with Graph Networks

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2002.09405.

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

pith.paper-citation-record.v1
2002.09405 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:45:30.197000Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

448
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 318a4edd-dd70-4231-a710-c9ab3471051d · inbound

Dynamical Data for More Efficient and Generalizable Learning: A Case Study in Disordered Elastic Networks cites this paper.

Dynamical Data for More Efficient and Generalizable Learning: A Case Study in Disordered Elastic Networks Learning to Simulate Complex Physics with Graph Networks

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-07T13:45:30.197000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:45:30.197000Z digest=sha256:62f060e9f602392230a28ddf34fbd8e42b9ad580181e572e5d00c9e7d29347b8

Observation 0b240076-3767-4425-b50f-7fdd72910282 · inbound

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches cites this paper.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Learning to Simulate Complex Physics with Graph Networks

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-06T19:20:44.506156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:20:44.506156Z digest=sha256:96a3ecde22dcaa2be4477f3d043dee6f8853015bd1d79f5e75bcdf99217584d2

Observation a671fdaf-0abf-4e7a-9392-dab7037b262f · inbound

Graph Neural Network Surrogates for Contacting Deformable Bodies with Necessary and Sufficient Contact Detection cites this paper.

Graph Neural Network Surrogates for Contacting Deformable Bodies with Necessary and Sufficient Contact Detection Learning to Simulate Complex Physics with Graph Networks

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-06T16:31:00.356393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:31:00.356393Z digest=sha256:34b25ea473bc31a0307db055fbb64c9a11eb06b3f435f6c6faae829dd2c03c69

Observation b70fd527-87bd-4c62-8aa7-a2132ce7acc5 · inbound

PAINET: A Principled Efficient Transformer for 3D Dynamics Modeling cites this paper.

PAINET: A Principled Efficient Transformer for 3D Dynamics Modeling Learning to Simulate Complex Physics with Graph Networks

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-05-18T10:01:13.623855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-18T09:57:31.774637Z digest=sha256:d4ae544bdd41834e83211b9a328d352c334934d0841ab8daf49837caeef64a8b

Observation 7a97983b-36ba-4f43-b32a-0731443b9aad · inbound

R5DGS: Semantic-Aware 4D Gaussian Splatting with Rigid Body Constraints for Efficient Dynamic Scene Reconstruction cites this paper.

R5DGS: Semantic-Aware 4D Gaussian Splatting with Rigid Body Constraints for Efficient Dynamic Scene Reconstruction Learning to Simulate Complex Physics with Graph Networks

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-06-29T22:44:01.261236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-29T22:43:47.031006Z digest=sha256:f79e7b2898208ab1cb30e5ea895bac30af0a83855ae04e44b1aafcc13be3722e

Observation fa86bcfd-2f7b-419a-855d-08d05d6c0d81 · inbound

Attention-based optimizer for symmetry finding cites this paper.

Attention-based optimizer for symmetry finding Learning to Simulate Complex Physics with Graph Networks

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-06-29T14:33:31.331279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-29T06:35:55.732968Z digest=sha256:51c2fa0b0184e8129f54495f01a078c327bb37daa24c63a91b2c7366434a88cf

Observation c68c420f-1c54-453e-af6e-c2f2edb5cc1a · inbound

Physically Viable World Models: A Case for Query-Conditioned Embodied AI cites this paper.

Physically Viable World Models: A Case for Query-Conditioned Embodied AI Learning to Simulate Complex Physics with Graph Networks

Reference 60

Resolution
verified exact
arxiv_id, observed 2026-06-29T09:13:16.544337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-29T06:55:57.801162Z digest=sha256:c76aaf99c2c01055274b30090b99f4b40c61ff5cbd4ee3b92255efba85f88e57

Observation 08d04187-0289-4195-86d0-1393b4e64dd2 · inbound

Attention mechanism for scalable mesh-based neural surrogates of free-surface fluids cites this paper.

Attention mechanism for scalable mesh-based neural surrogates of free-surface fluids Learning to Simulate Complex Physics with Graph Networks

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-06-26T06:19:03.844593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-26T06:10:09.472925Z digest=sha256:cee084ea6ce004b5337fcfb982738c54bbbcf9210ad87de02b5bac8dda7cf3ec

Observation 74429b51-0385-46fd-afb7-21f7376d1204 · inbound

Label-Free Finite-Volume-Residual Training of Attention Graph Neural Networks for Coupled Thermo-Fluid Fields cites this paper.

Label-Free Finite-Volume-Residual Training of Attention Graph Neural Networks for Coupled Thermo-Fluid Fields Learning to Simulate Complex Physics with Graph Networks

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-01T10:13:39.803771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T10:13:39.803771Z digest=sha256:246ca9775b8dc9663930873805cadd1828ec57c66fcf1ced823695b1d1222231

Observation 4103668e-4860-438a-b0a8-0cddca48fbd9 · inbound

Hybrid Lagrangian-Eulerian Model for Lagrangian Fluid Simulation cites this paper.

Hybrid Lagrangian-Eulerian Model for Lagrangian Fluid Simulation Learning to Simulate Complex Physics with Graph Networks

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-06T00:30:47.327874Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T00:30:47.327874Z digest=sha256:1321ebd12a811c17918ff430fd4c16a9cfd056890085e6c3de1c4216d739b16a

Observation f3b6fc2e-69e1-42f2-8715-c95bb0e63be2 · inbound

Enactive Artificial Intelligence: A Decision-Centric Architecture for Complex Systems cites this paper.

Enactive Artificial Intelligence: A Decision-Centric Architecture for Complex Systems Learning to Simulate Complex Physics with Graph Networks

Reference 121

Resolution
unresolved
no resolver link, observed 2026-08-05T19:30:33.780115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T19:30:33.780115Z digest=sha256:cab1e504f9a14763e851113dd674f6c1f9928b76a31d8ed246bf6cbf420bd131