Paper Citation Record · LEDGER

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes

As of 31 July 2026, this Paper Citation Record lists 47 of 47 outbound references and 1 inbound Pith citation observation for arXiv:2604.08586.

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

pith.paper-citation-record.v1
2604.08586 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-14T21:20:56.281902Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-07-30T06:33:22.917629+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T05:11:53.271385Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-04T13:29:51.567152Z

Reference resolution

47 of 47 outbound references displayed

verified exact10
verified fuzzy36
unresolved0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation aa04423c-58d2-4d50-b6f5-0ddafce43e2f · outbound

This paper cites Butterworth-Heinemann.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Butterworth-Heinemann

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.188550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation c175c3e3-b20f-4df1-8f08-195dcbd430a4 · outbound

This paper cites Cambridge University Press.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Cambridge University Press

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.179334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation b35dc1d9-6ad0-4e9f-87af-c631d7a14f97 · outbound

This paper cites MIT Press, Cambridge, MA.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes MIT Press, Cambridge, MA

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.181038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 6aba764b-1655-4ba2-a9bd-6fcf22a3350a · outbound

This paper cites Machine learning for fluid mechanics.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Machine learning for fluid mechanics

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.184976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 5a7d06f3-260f-4735-ba66-47aa97a3ce89 · outbound

This paper cites Improving aircraft performance using machine learning: A review.Aerospace Science and Technology, 138:108354.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Improving aircraft performance using machine learning: A review.Aerospace Science and Technology, 138:108354

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.186916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 870f885e-8da8-4e8d-bfeb-e9dca833a847 · outbound

This paper cites Physics-informed machine learning.Nature Reviews Physics, 3(6):422–440.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Physics-informed machine learning.Nature Reviews Physics, 3(6):422–440

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.190484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 7274fe35-a43a-4b26-baac-5bfd7aa9a263 · outbound

This paper cites Learning mesh-based simulation with graph networks.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Learning mesh-based simulation with graph networks

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.182798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation e81933e0-dc01-400f-befa-83773851da49 · outbound

This paper cites Graph neural networks for the prediction of aircraft surface pressure distributions.Aerospace Science and Technology, 137:108268.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Graph neural networks for the prediction of aircraft surface pressure distributions.Aerospace Science and Technology, 137:108268

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.199282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 185ee285-f0e0-45ed-b040-96611d8dc40e · outbound

This paper cites Surrogate modeling of the aerodynamic performance for airfoils in transonic regime.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Surrogate modeling of the aerodynamic performance for airfoils in transonic regime

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.212988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation f28ebb2a-c4e5-4f54-afbc-16ee87ea49f2 · outbound

This paper cites A certifiable machine learning-based pipeline to predict fatigue life of aircraft structures.Engineering Failure Analysis, page 110334.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes A certifiable machine learning-based pipeline to predict fatigue life of aircraft structures.Engineering Failure Analysis, page 110334

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.248277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation e2f89931-09b7-4c36-95fa-3b6519c6dfde · outbound

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

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Fourier Neural Operator for Parametric Partial Differential Equations

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-05-14T21:22:59.270251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation c5b01a7e-6d13-4df7-9001-5ca14bed92a9 · outbound

This paper cites Transfer learning-enhanced deep reinforcement learning for aerodynamic airfoil optimization subject to structural constraints.Physics of Fluids, 37(8).

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Transfer learning-enhanced deep reinforcement learning for aerodynamic airfoil optimization subject to structural constraints.Physics of Fluids, 37(8)

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.223739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation e00ec608-a8f7-46b5-be2b-39f6a4ed5a9e · outbound

This paper cites Generative artificial intelligence.Electronic markets, 33(1):63.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Generative artificial intelligence.Electronic markets, 33(1):63

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.195220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 5cb8fdd1-a698-4d2f-9730-5426cc8afec3 · outbound

This paper cites Generative adversarial nets.Advances in neural information processing systems, 27.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Generative adversarial nets.Advances in neural information processing systems, 27

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.245425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation b57b94fd-dab1-4161-8bf6-2193e8e04b45 · outbound

This paper cites Auto-Encoding Variational Bayes.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Auto-Encoding Variational Bayes

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-05-14T21:22:59.240137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 6b06f3c1-fba9-4c68-b676-fcacb74e39ff · outbound

This paper cites Attention is all you need.Advances in neural information processing systems, 30.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Attention is all you need.Advances in neural information processing systems, 30

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.226523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 69d6a96b-9200-47ba-91c4-b7ec78788fe0 · outbound

This paper cites Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.259886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 14d2773c-2112-471a-b440-e59ad93a8cd8 · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Score-Based Generative Modeling through Stochastic Differential Equations

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-05-14T21:22:59.235928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation ea69bdb9-e37c-4de7-a7ec-8c8e4935daae · outbound

This paper cites High- resolution image synthesis with latent diffusion models.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes High- resolution image synthesis with latent diffusion models

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.229500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 4f5a9f1f-118c-4df6-9ebd-f5a5a48cdcbd · outbound

This paper cites Flow Matching for Generative Modeling.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Flow Matching for Generative Modeling

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-05-14T21:22:59.249506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 5908f87b-d6d8-4e70-ade1-2b25e2884f95 · outbound

This paper cites FourierFlow: Frequency-aware Flow Matching for Generative Turbulence Modeling.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes FourierFlow: Frequency-aware Flow Matching for Generative Turbulence Modeling

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:22:59.231147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 74c14d80-484d-42e8-8982-110c672aa467 · outbound

This paper cites tempogan: A temporally coherent, volumetric gan for super-resolution fluid flow.ACM Transactions on Graphics (TOG), 37(4):1–15.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes tempogan: A temporally coherent, volumetric gan for super-resolution fluid flow.ACM Transactions on Graphics (TOG), 37(4):1–15

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.236468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 45028dbb-24d3-40fc-91cc-2f862a1ff9cd · outbound

This paper cites Generative AI for fast and accurate statistical computation of fluids.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Generative AI for fast and accurate statistical computation of fluids

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:22:59.265783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation d6ce3244-4d61-4e4b-bb67-6d1a5eca43a5 · outbound

This paper cites Ai-based generative algorithms applied to the design of blended wing body aircraft.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Ai-based generative algorithms applied to the design of blended wing body aircraft

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.240326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 7c0a0817-273b-4f99-9aea-365b1b352ac9 · outbound

This paper cites Airfoil Diffusion: Denoising Diffusion Model For Conditional Airfoil Generation.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Airfoil Diffusion: Denoising Diffusion Model For Conditional Airfoil Generation

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T21:22:59.253744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 7857f74d-c38e-42f8-8611-de8fd38fd5bd · outbound

This paper cites Exploring denoising diffusion models for compressible fluid field prediction.Computers & Fluids, 298:106665.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Exploring denoising diffusion models for compressible fluid field prediction.Computers & Fluids, 298:106665

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.255253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 704f1a26-da30-445b-ad47-79110b2a1643 · outbound

This paper cites Uncertainty-aware surrogate models for airfoil flow simulations with denoising diffusion probabilistic models.AIAA Journal, 62(8):2912–2933.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Uncertainty-aware surrogate models for airfoil flow simulations with denoising diffusion probabilistic models.AIAA Journal, 62(8):2912–2933

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.201415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation aa83ca1e-0e62-4343-bf4e-cfb15f315880 · outbound

This paper cites Aerodit: Diffusion transformers for reynolds-averaged navier–stokes simulations of airfoil flows.Physics of Fluids, 37(12).

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Aerodit: Diffusion transformers for reynolds-averaged navier–stokes simulations of airfoil flows.Physics of Fluids, 37(12)

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.208388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 59ea8c86-2a5e-4319-ae38-2af66bb87b63 · outbound

This paper cites Foildiff: A hybrid diffusion transformer model for airfoil flow field prediction.Aerospace Science and Technology, page 111677.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Foildiff: A hybrid diffusion transformer model for airfoil flow field prediction.Aerospace Science and Technology, page 111677

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.216845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation c6f1c7ac-ee14-4b75-ae8e-b86dc82e273c · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes U-net: Convolutional networks for biomedical image segmentation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.203733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation d46ffd48-5dd4-43c5-81c1-6c4fef04aba4 · outbound

This paper cites Classifier-Free Diffusion Guidance.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Classifier-Free Diffusion Guidance

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-05-14T21:22:59.245121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 81cc7554-e327-4898-84a6-971f57a1f268 · outbound

This paper cites A comparative study of learning techniques for the compressible aerodynamics over a transonic rae2822 airfoil.Computers & Fluids, 251:105759.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes A comparative study of learning techniques for the compressible aerodynamics over a transonic rae2822 airfoil.Computers & Fluids, 251:105759

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.250620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 3abe0b19-e986-4fd6-9322-05e30dccfd7f · outbound

This paper cites Onera’s crm wbpn database for machine learning activities, related regression challenge and first results.Computers & Fluids, 302:106838.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Onera’s crm wbpn database for machine learning activities, related regression challenge and first results.Computers & Fluids, 302:106838

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.197239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 8c62d003-4256-4f9b-8f39-9351e188eb7f · outbound

This paper cites Scalable diffusion models with transformers.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Scalable diffusion models with transformers

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.206188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 1b6225d6-eb40-4fb6-8453-b8707f1e5d31 · outbound

This paper cites Root mean square layer normalization.Advances in neural infor- mation processing systems, 32.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Root mean square layer normalization.Advances in neural infor- mation processing systems, 32

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.210600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation aad0a6e4-3636-4b32-b9a5-3d8e3d899c31 · outbound

This paper cites GLU Variants Improve Transformer.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes GLU Variants Improve Transformer

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-05-14T21:22:59.261222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 6b28bb1a-6901-40d9-824b-4dc87f521fb7 · outbound

This paper cites Reconstruction vs.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Reconstruction vs

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.214830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 181eb0b1-588e-4343-987c-8f2e322ee371 · outbound

This paper cites Scaling vision transformers to 22 billion parameters.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Scaling vision transformers to 22 billion parameters

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.219307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 2a5efe16-50b5-4b3c-b124-a70a1f84cfd9 · outbound

This paper cites Transformers are rnns: Fast autoregressive transformers with linear attention.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Transformers are rnns: Fast autoregressive transformers with linear attention

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.253197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 2f75f053-38a2-4495-98d2-05b43253baf5 · outbound

This paper cites SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-15T00:56:50.121098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation cd4aa035-e911-42d0-9fd8-9a582efedf52 · outbound

This paper cites 127169.459.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes 127169.459

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.221439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 895870df-7b74-42f7-a043-de5a1bc7b23d · outbound

This paper cites Learning nonlinear operators via deeponet based on the universal approximation theorem of operators.Nature machine intelligence, 3(3):218–229.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Learning nonlinear operators via deeponet based on the universal approximation theorem of operators.Nature machine intelligence, 3(3):218–229

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.243168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 0485b72f-8526-46c1-ac7f-e794cd542cd0 · outbound

This paper cites Fourier neural operator with learned deformations for pdes on general geometries.Journal of Machine Learning Research, 24(388):1–26.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Fourier neural operator with learned deformations for pdes on general geometries.Journal of Machine Learning Research, 24(388):1–26

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.257830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation e87087af-7431-431d-947b-e631b39fb87c · outbound

This paper cites Neural operators for accelerating scientific simulations and design.Nature Reviews Physics, 6(5):320–328.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Neural operators for accelerating scientific simulations and design.Nature Reviews Physics, 6(5):320–328

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.192523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 862243cf-3b59-43e8-b401-8ea87419f542 · outbound

This paper cites Optuna: A next-generation hyperparameter optimization framework.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Optuna: A next-generation hyperparameter optimization framework

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.231718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 1d9b40d0-2b45-4cc2-a778-c1d587d4077d · outbound

This paper cites Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-06-02T03:03:57.525930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 04ded822-681a-4b6a-a00f-62efc1562c6b · outbound

This paper cites Accelerate: Training and inference at scale made simple, efficient and adaptable.https://github.com/huggingface/accelerate.

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Accelerate: Training and inference at scale made simple, efficient and adaptable.https://github.com/huggingface/accelerate

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T23:49:35.234097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Pith citing papers

Observation 19275f23-f41d-40b7-978b-1cc012742a38 · inbound

Kolmogorov Arnold networks (KAN) for aerodynamic prediction: a comparison with MLPs and GNNs cites this paper.

Kolmogorov Arnold networks (KAN) for aerodynamic prediction: a comparison with MLPs and GNNs FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-07-04T13:29:51.568527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.