Paper Citation Record · LEDGER

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations

As of 18 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 1 inbound Pith citation observation for arXiv:2505.14704.

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

pith.paper-citation-record.v1
2505.14704 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:42:00.856849Z

measured 51 of 51 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-31T12:51:45.038939Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

50 of 50 outbound references displayed

verified exact1
verified fuzzy23
unresolved26
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9b335440-6505-452b-a0fd-442f5e2ffdb6 · outbound

This paper cites an unresolved cited work.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Unresolved cited work

Reference 1

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Observation 6f9e86e0-2f5a-4328-a468-236761ff9876 · outbound

This paper cites Berkooz, P.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Berkooz, P

Reference 2

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Observation 90db892a-82f3-424c-ba7e-ef962218d5bd · outbound

This paper cites Saves, R.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Saves, R

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:42:01.775230Z

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Observation 8d8cf959-7092-4d00-9dc4-30413f74464c · outbound

This paper cites Fossati, Evaluation of aerodynamic loads via reduced-order method- ology, AIAA Journal 53 (2015) 2389–2405.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Fossati, Evaluation of aerodynamic loads via reduced-order method- ology, AIAA Journal 53 (2015) 2389–2405

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:42:01.755465Z

Source-reported events for the cited work

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Observation 868f9ecc-241e-4765-8990-b88b3a8a6939 · outbound

This paper cites Ghoreyshi, P.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Ghoreyshi, P

Reference 5

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Observation fc9a4524-d8bb-421b-a064-e2670bce4404 · outbound

This paper cites Catalani, D.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Catalani, D

Reference 6

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Source-reported events for the cited work

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Observation 1dd39d64-7332-420e-a94e-d8f89741e4e1 · outbound

This paper cites Casenave, B.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Casenave, B

Reference 7

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Observation ed66ab49-3afd-4236-a930-99cc9bae2e54 · outbound

This paper cites Dupuis, J.-C.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Dupuis, J.-C

Reference 8

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Source-reported events for the cited work

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Observation 170bd123-9cc2-43c8-b280-10ce85aab6c6 · outbound

This paper cites Decoding complexity: how machine learning is redefining scientific discovery.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Decoding complexity: how machine learning is redefining scientific discovery

Reference 9

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Observation 01bb12ae-ee31-49c5-b11f-30e43c53990e · outbound

This paper cites Martin, T.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Martin, T

Reference 10

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Source-reported events for the cited work

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Observation 3ad1cbd1-2e7f-4eed-abb6-3d4cd4e6c5bc · outbound

This paper cites Xu, X.-H.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Xu, X.-H

Reference 11

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Source-reported events for the cited work

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Observation e2e655a1-f9b9-4241-ad02-75923d2afa76 · outbound

This paper cites de Zordo-Banliat, G.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations de Zordo-Banliat, G

Reference 12

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Observation 7e0d9cd1-aa4e-4d56-b8ed-f1368e56cc09 · outbound

This paper cites Ajuria Illarramendi, A.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Ajuria Illarramendi, A

Reference 13

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Source-reported events for the cited work

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Observation ee42779d-d4a8-469a-bd50-19d038cb57e2 · outbound

This paper cites Agarwal, N.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Agarwal, N

Reference 14

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Source-reported events for the cited work

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Observation 04b15da1-6381-43b7-8198-389e0ec1251b · outbound

This paper cites Dias Ribeiro, M.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Dias Ribeiro, M

Reference 15

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Source-reported events for the cited work

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Observation e6b623a8-bc66-4471-bca7-d4342e0e8a6e · outbound

This paper cites Bertrand, F.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Bertrand, F

Reference 16

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Source-reported events for the cited work

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Observation 166470d7-322f-4cde-a2e5-184ac71ee532 · outbound

This paper cites an unresolved cited work.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Unresolved cited work

Reference 17

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Source-reported events for the cited work

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Observation dc3e3f94-8fa3-4837-b3b8-bb68272ddf59 · outbound

This paper cites LeCun, Y.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations LeCun, Y

Reference 18

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Observation fd5b1b2f-8db8-4fb6-8483-8c1cddaa3733 · outbound

This paper cites Ronneberger, P.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Ronneberger, P

Reference 19

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Source-reported events for the cited work

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Observation cabc1c2f-e857-4249-9104-307b7931a1b3 · outbound

This paper cites an unresolved cited work.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Unresolved cited work

Reference 20

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Observation 9ddaa1eb-efcf-4bf6-a955-1a0341702a75 · outbound

This paper cites Fesquet, M.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Fesquet, M

Reference 21

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Source-reported events for the cited work

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Observation c7c463d0-49d2-4246-815e-24f18adf5031 · outbound

This paper cites an unresolved cited work.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Unresolved cited work

Reference 22

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Source-reported events for the cited work

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Observation 998fa4bd-90b0-47ad-8d40-f8335fe2dd9f · outbound

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

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Learning Mesh-Based Simulation with Graph Networks

Reference 23

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Observation fbc5374e-b6da-449e-b7af-ecb4ee8d9ab9 · outbound

This paper cites an unresolved cited work.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Unresolved cited work

Reference 24

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Observation 4f4da4c4-1f93-4589-9969-f6addaa3bc76 · outbound

This paper cites MultiScale MeshGraphNets.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations MultiScale MeshGraphNets

Reference 25

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Observation e1177576-01d9-4811-afec-0696f5f8183c · outbound

This paper cites an unresolved cited work.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Unresolved cited work

Reference 26

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Observation 5ef0b23c-aa8f-4336-9f10-f11a59761b13 · outbound

This paper cites Hines, P.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Hines, P

Reference 27

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Observation 068c2c87-f86d-4238-9515-1e3fe9b9bca5 · outbound

This paper cites Azizzadenesheli, N.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Azizzadenesheli, N

Reference 28

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Observation 0ea37876-861a-4e56-84eb-a822a6a8c489 · outbound

This paper cites Fourier Neural Operator for Parametric Partial Differential Equations.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Fourier Neural Operator for Parametric Partial Differential Equations

Reference 29

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Observation 43f362fe-57f1-46a2-9795-82278f94d798 · outbound

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Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Unresolved cited work

Reference 30

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Observation 8ce40999-1eb3-4912-bfa3-aea3753cd2ad · outbound

This paper cites an unresolved cited work.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Unresolved cited work

Reference 31

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Observation f1a5f4fe-a57f-4c9e-8b0e-bc9ab17e596f · outbound

This paper cites Serrano, L.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Serrano, L

Reference 32

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Source-reported events for the cited work

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Observation eab4c8d6-0964-4c7c-92d7-e13c83d61fae · outbound

This paper cites Catalani, S.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Catalani, S

Reference 33

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Observation 53b1c91d-294d-49a0-bf4a-1cfc95fad4a8 · outbound

This paper cites AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutions.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutions

Reference 34

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Observation 46a7f153-8539-48c5-8cee-bb8e117a60cd · outbound

This paper cites Jacot, F.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Jacot, F

Reference 35

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Observation e743bf9a-8ee7-49c0-8222-023acca4c567 · outbound

This paper cites Tancik, P.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Tancik, P

Reference 36

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Observation 55d321b8-9fdb-4ef0-9c42-28833da813d0 · outbound

This paper cites Zintgraf, K.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Zintgraf, K

Reference 37

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Observation 1be329e1-2966-4a45-b3b7-841de8f0f555 · outbound

This paper cites From data to functa: Your data point is a function and you can treat it like one.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations From data to functa: Your data point is a function and you can treat it like one

Reference 38

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Observation 91677d5d-c377-4f03-ab7f-f8e940c25ce7 · outbound

This paper cites Jasak, Openfoam: Open source cfd in research and industry, In- ternational journal of naval architecture and ocean engineering 1 (2009) 89–94.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Jasak, Openfoam: Open source cfd in research and industry, In- ternational journal of naval architecture and ocean engineering 1 (2009) 89–94

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:42:01.259304Z

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Observation 319bbc64-326e-4cab-8adc-84d0bc919828 · outbound

This paper cites Spalart, S.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Spalart, S

Reference 40

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Observation 52363687-1380-4759-8f44-faefaa557c7e · outbound

This paper cites Gardner, G.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Gardner, G

Reference 41

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Observation 26df390e-e3c3-4597-bd1e-94451fefad60 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Adam: A Method for Stochastic Optimization

Reference 42

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Observation 0b307285-ba44-454d-8eb2-f1ed86512e57 · outbound

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Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Unresolved cited work

Reference 43

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Observation 849ce70a-55b3-40a7-95e8-8e112eb4338c · outbound

This paper cites Hamilton, Z.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Hamilton, Z

Reference 44

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Observation aa382b7a-bb16-4437-8386-58e138ea3700 · outbound

This paper cites Transolver: A Fast Transformer Solver for PDEs on General Geometries.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Transolver: A Fast Transformer Solver for PDEs on General Geometries

Reference 45

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Observation a7666bfd-1338-46c5-836a-224e836fa46a · outbound

This paper cites an unresolved cited work.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Unresolved cited work

Reference 46

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Observation 56a9e8c7-271a-4a9c-88e0-08a7ada1a482 · outbound

This paper cites Sabater, P.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Sabater, P

Reference 47

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Observation ec69e523-6299-48cd-9028-48c504f40e68 · outbound

This paper cites NeurIPS 2024 ML4CFD Competition: Harnessing Machine Learning for Computational Fluid Dynamics in Airfoil Design.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations NeurIPS 2024 ML4CFD Competition: Harnessing Machine Learning for Computational Fluid Dynamics in Airfoil Design

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-15T21:42:00.849192Z

Source-reported events for the cited work

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Observation 134706a1-7d4d-4482-9f69-0462ffed25c0 · outbound

This paper cites PLAID: A Unified Data Model for Machine Learning on Heterogeneous Physics Simulations.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations PLAID: A Unified Data Model for Machine Learning on Heterogeneous Physics Simulations

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-15T21:42:00.856849Z

Source-reported events for the cited work

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Observation 9725b3cb-7717-4c26-aa89-a665a6e88847 · outbound

This paper cites an unresolved cited work.

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations Unresolved cited work

Reference 5261

Resolution: unresolved; no resolver link, observed 2026-08-15T21:42:00.843110Z

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Pith citing papers

Observation b0300d16-7a25-459a-a6fc-83c90c1736d4 · inbound

Physics Transformer: Tailoring Transformer for General PDE Prediction cites this paper.

Physics Transformer: Tailoring Transformer for General PDE Prediction Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations

Reference 28

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Source-reported events for the cited work

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