Paper Citation Record · LEDGER

Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates

As of 7 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2507.01057.

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

pith.paper-citation-record.v1
2507.01057 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:59:16.729994Z

measured 18 of 18 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 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

18 of 18 outbound references displayed

verified exact4
verified fuzzy7
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 12d906c9-5fc8-42eb-8bcb-9cee595559cc · outbound

This paper cites CFD Vision 2030 Study: A Path to Revolutionary Computational Aerosciences.

Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates CFD Vision 2030 Study: A Path to Revolutionary Computational Aerosciences

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:59:18.955827Z

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.

Observation 7995f018-7da9-459b-a684-0c890b6fc023 · outbound

This paper cites DGM: A deep learning algorithm for solving partial differential equations.

Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates DGM: A deep learning algorithm for solving partial differential equations

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T21:59:15.470151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72c1cbd0-91ee-4ca4-94a6-3289b15f0013 · outbound

This paper cites Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows.

Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T21:59:15.533768Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 310cc5e1-b2e5-4c8c-ada3-e90938ec2809 · outbound

This paper cites Mesh Optimization.

Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates Mesh Optimization

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:59:18.741506Z

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.

Observation ee101241-893b-4463-9cd7-ee894173f278 · outbound

This paper cites A parallel parameterized level set topology opti- mization framework for large-scale structures with unstructured meshes.

Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates A parallel parameterized level set topology opti- mization framework for large-scale structures with unstructured meshes

Reference 5

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T21:59:17.935148Z

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.

Observation dfd7eae6-bdf0-4eb3-ad7a-894b501713ab · outbound

This paper cites Network In Network.

Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates Network In Network

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T21:59:15.801379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f49cb33-2eac-4e2f-9d57-966b14e40469 · outbound

This paper cites Geometric parameters in the target matrix mesh optimization paradigm.

Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates Geometric parameters in the target matrix mesh optimization paradigm

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T21:59:15.888727Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0c63245-5626-4069-8067-dbd0f806d25f · outbound

This paper cites A novel neural network approach for airfoil mesh quality evaluation.

Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates A novel neural network approach for airfoil mesh quality evaluation

Reference 8

Resolution: verified exact; doi, observed 2026-08-06T21:59:17.154795Z

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.

Observation 0ef92fc8-1cd9-477e-b99f-22e9f91ae043 · outbound

This paper cites FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation.

Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:59:18.623580Z

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.

Observation bc83d58c-d742-44d2-9373-eb814ea54bbc · outbound

This paper cites AtlasNet: A Papier-M\^ach\'e Approach to Learning 3D Surface Generation.

Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates AtlasNet: A Papier-M\^ach\'e Approach to Learning 3D Surface Generation

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:59:17.594197Z

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.

Observation 3a9dbddb-85a2-44c3-9f40-351b3fababf4 · outbound

This paper cites In:ACM Transactions on Graphics38.4 (July 2019).

Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates In:ACM Transactions on Graphics38.4 (July 2019)

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T21:59:16.259178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc3659b4-dc23-4d1f-87a7-44b82f4fd1df · outbound

This paper cites Neural Mesh Flow: 3D Manifold Mesh GenerationviaDiffeomorphicFlows.Tech.rep.2020.Availableat<https://kunalmgupta.

Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates Neural Mesh Flow: 3D Manifold Mesh GenerationviaDiffeomorphicFlows.Tech.rep.2020.Availableat<https://kunalmgupta

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:59:18.495552Z

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.

Observation 4b7d9be5-ce8c-404d-88be-cb40a05fa4b8 · outbound

This paper cites MeshDQN: A Deep Reinforcement Learning Framework for Improving Meshes in Computational Fluid Dynamics.

Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates MeshDQN: A Deep Reinforcement Learning Framework for Improving Meshes in Computational Fluid Dynamics

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:59:17.315110Z

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.

Observation 4b6e0bb0-8562-4cdb-ab8f-bb0a8a202689 · outbound

This paper cites Reinforcement Learning for Adaptive Mesh Refinement.

Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates Reinforcement Learning for Adaptive Mesh Refinement

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:59:18.386614Z

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.

Observation f9030b0a-5285-4fd2-8756-63aad1b7cc1f · outbound

This paper cites Swarm Reinforcement Learning for Adaptive Mesh Refinement.

Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates Swarm Reinforcement Learning for Adaptive Mesh Refinement

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:59:18.270944Z

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.

Observation b252227a-eca2-41da-9837-12bca9971d8a · outbound

This paper cites Raissi, P.

Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates Raissi, P

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T21:59:16.564231Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b4989ed-af9f-4e26-986c-a97039d5a1d0 · outbound

This paper cites An Improved Structured Mesh Generation Method Based on Physics-informed Neural Networks.

Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates An Improved Structured Mesh Generation Method Based on Physics-informed Neural Networks

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:59:16.983460Z

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.

Observation b901dee5-b1da-48c8-a286-b894bb0ec4f6 · outbound

This paper cites Understanding the difficulty of training deep feedfor- ward neural networks.

Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates Understanding the difficulty of training deep feedfor- ward neural networks

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:59:18.128005Z

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.

Pith citing papers

No inbound Pith citation observations are available.