Reconstructing 3D Flow from 2D Data with Diffusion Transformer | Paper Citation Record

Observation 39b1b94e-0237-4b1a-9527-914897f6704a · outbound

This paper cites Scattering particle characteristics and their effect on pulsed laser measurements of fluid flow: speckle velocimetry vs particle image velocimetry.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Scattering particle characteristics and their effect on pulsed laser measurements of fluid flow: speckle velocimetry vs particle image velocimetry

Reference 1

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Observation ca6aa7a9-d843-4199-8a6c-21b81c81b6d7 · outbound

This paper cites Neural operator: Graph kernel network for partial differential equations.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Neural operator: Graph kernel network for partial differential equations

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.747089Z

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Observation e208d86b-51d8-4b61-9905-a6f35256423d · outbound

This paper cites All are worth words: A vit backbone for diffusion models.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer All are worth words: A vit backbone for diffusion models

Reference 3

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Observation b9c5024c-2bed-4bf9-bbb6-052aa7bcb304 · outbound

This paper cites One transformer fits all distributions in multi-modal diffusion at scale.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer One transformer fits all distributions in multi-modal diffusion at scale

Reference 4

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Observation 0876136f-c35e-48e8-9e2b-937144c544c9 · outbound

This paper cites Machine learning for fluid mechanics.Annual review of fluid mechanics, 52(1):477–508, 2020.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Machine learning for fluid mechanics.Annual review of fluid mechanics, 52(1):477–508, 2020

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T11:04:46.669890Z

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Observation 313f8d18-b43a-45a4-b6ef-32d8897077a6 · outbound

This paper cites Mo- tion2vecsets: 4d latent vector set diffusion for non-rigid shape reconstruction and tracking.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Mo- tion2vecsets: 4d latent vector set diffusion for non-rigid shape reconstruction and tracking

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.716001Z

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Observation 6676b9c4-c122-494b-924c-05f410603f82 · outbound

This paper cites Pixart-alpha: Fast training of diffusion transformer for photorealistic text-to-image synthesis.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Pixart-alpha: Fast training of diffusion transformer for photorealistic text-to-image synthesis

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.704644Z

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Observation 7f8aac22-66f6-4003-b086-b765d5deb349 · outbound

This paper cites Generative pretraining from pixels.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Generative pretraining from pixels

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T11:04:46.680664Z

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Observation 1407a6dc-7e68-474e-8e30-e33694059493 · outbound

This paper cites Nerdi: Single-view nerf synthesis with language- guided diffusion as general image priors.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Nerdi: Single-view nerf synthesis with language- guided diffusion as general image priors

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.687321Z

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Observation bfeb2215-4390-408c-becb-a5ede535a5a3 · outbound

This paper cites Diffusion models beat gans on image synthesis.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Diffusion models beat gans on image synthesis

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T11:04:46.687358Z

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Observation 3aea973a-9c52-4d4b-8eec-22ada34fc5c9 · outbound

This paper cites NICE: Non-linear Independent Components Estimation.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer NICE: Non-linear Independent Components Estimation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T11:04:46.691057Z

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Observation 2671fa83-db2a-4cb6-bdd2-3d7890fd74e8 · outbound

This paper cites Generalization capabilities of conditional GAN for turbulent flow under changes of geometry.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Generalization capabilities of conditional GAN for turbulent flow under changes of geometry

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-11T11:04:47.111865Z

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Observation 40c2b2fc-1a3e-4fe7-a7cd-636a6a083abe · outbound

This paper cites Turbulence modeling in the age of data.Annual review of fluid mechanics, 51(1):357–377, 2019.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Turbulence modeling in the age of data.Annual review of fluid mechanics, 51(1):357–377, 2019

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.669098Z

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Observation e0a07acf-bbbc-4c2f-8a87-1ce3286a315e · outbound

This paper cites Reinforcement learning for bluff body active flow control in experi- ments and simulations.Proceedings of the National Academy of Sciences, 117(42): 26091–26098, 2020.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Reinforcement learning for bluff body active flow control in experi- ments and simulations.Proceedings of the National Academy of Sciences, 117(42): 26091–26098, 2020

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.658243Z

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Observation 3da28e12-6343-4c0b-b992-1399dc59461f · outbound

This paper cites 3d shape induction from 2d views of multiple objects.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer 3d shape induction from 2d views of multiple objects

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.647074Z

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Observation 4036a41f-0131-4299-9850-a8017df6eb96 · outbound

This paper cites Get3d: A generative model of high quality 3d textured shapes learned from images.Advances In Neural Information Processing Systems, 35:31841–31854, 2022.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Get3d: A generative model of high quality 3d textured shapes learned from images.Advances In Neural Information Processing Systems, 35:31841–31854, 2022

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.635947Z

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Observation 05d3d00e-251e-46c7-b0b6-0d8168aaf8a7 · outbound

This paper cites Generative adversarial networks.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Generative adversarial networks

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.623501Z

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Observation a1dd7aa5-0a2f-4430-89b8-7928dff2ec34 · outbound

This paper cites Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T11:04:46.716370Z

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Observation 4a890c8c-3ec5-4f2d-ae64-17b49d8dbdc0 · outbound

This paper cites Convolutional-network models to predict wall-bounded turbulence from wall quantities.Journal of Fluid Mechanics, 928:A27, 2021.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Convolutional-network models to predict wall-bounded turbulence from wall quantities.Journal of Fluid Mechanics, 928:A27, 2021

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.612563Z

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Observation 514961e5-dcc4-4524-96fb-524c53b735a9 · outbound

This paper cites From coarse wall measurements to turbulent velocity fields through deep learning.Physics of fluids, 33(7), 2021.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer From coarse wall measurements to turbulent velocity fields through deep learning.Physics of fluids, 33(7), 2021

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.600117Z

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Observation e1c6a01f-ea9a-4147-b215-9675822022da · outbound

This paper cites Active control for drag reduction of turbulent channel flow based on convolutional neural networks.Physics of Fluids, 32(9), 2020.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Active control for drag reduction of turbulent channel flow based on convolutional neural networks.Physics of Fluids, 32(9), 2020

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.588556Z

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Observation a112edf1-670b-4996-bb69-34e74bd26fd1 · outbound

This paper cites Gnot: A general neural operator transformer for operator learning.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Gnot: A general neural operator transformer for operator learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.577122Z

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Observation 3cfdeaff-bae7-415a-b0e7-b939f0baca47 · outbound

This paper cites Denoising diffusion probabilistic models.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Denoising diffusion probabilistic models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T11:04:46.736079Z

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Observation 9922dc45-c791-4797-848e-245225868003 · outbound

This paper cites Zero-shot text-guided object generation with dream fields.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Zero-shot text-guided object generation with dream fields

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.559390Z

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Observation c8a8030c-fdb6-46c8-a61d-14f05df89349 · outbound

This paper cites Near-wall turbulence.Physics of Fluids, 25(10), 2013.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Near-wall turbulence.Physics of Fluids, 25(10), 2013

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.548614Z

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Observation 4e4fca2b-d793-441b-b4a4-bac916586a2d · outbound

This paper cites 3d gaussian splatting for real-time radiance field rendering.ACM Trans.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer 3d gaussian splatting for real-time radiance field rendering.ACM Trans

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T11:04:46.746471Z

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Observation 9b176ef2-705b-4711-ad20-01fca08afbd9 · outbound

This paper cites Deep fluids: A generative network for parameterized fluid simulations.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Deep fluids: A generative network for parameterized fluid simulations

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T11:04:46.750221Z

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Observation 2e5b5e32-70e1-4e3f-9c71-f976a1234edf · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Adam: A Method for Stochastic Optimization

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T11:04:46.753904Z

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Observation b9e5e159-7345-449f-b6de-7333f0e92395 · outbound

This paper cites Auto-Encoding Variational Bayes.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Auto-Encoding Variational Bayes

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T11:04:46.757877Z

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Observation 25c26fb5-a4e8-4c3f-8088-3b75e07e26bd · outbound

This paper cites Neural operator: Learning maps between function spaces with applications to pdes.Journal of Machine Learning Research, 24(89):1–97, 2023.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Neural operator: Learning maps between function spaces with applications to pdes.Journal of Machine Learning Research, 24(89):1–97, 2023

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.524874Z

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Observation 93bf27d6-9440-4af0-a7e8-59908a1451a4 · outbound

This paper cites Deep learning in fluid dynamics.Journal of Fluid Mechanics, 814: 1–4, 2017.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Deep learning in fluid dynamics.Journal of Fluid Mechanics, 814: 1–4, 2017

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.514018Z

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Observation 8cff165d-7085-4405-9f40-b46b31f2f5b8 · outbound

This paper cites Deep learning.nature, 521 (7553):436–444, 2015.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Deep learning.nature, 521 (7553):436–444, 2015

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.502473Z

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Observation 1e4e6352-e054-4d86-b6fa-0e0dbaf798c6 · outbound

This paper cites Transformer for Partial Differential Equations' Operator Learning.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Transformer for Partial Differential Equations' Operator Learning

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-11T11:04:46.772380Z

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Observation 27c2eefc-9d21-4537-9b29-affd11491867 · outbound

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

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Fourier Neural Operator for Parametric Partial Differential Equations

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T11:04:46.776370Z

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Observation c433abaf-8493-4748-93c1-537c4b46a936 · outbound

This paper cites From zero to turbulence: Generative modeling for 3d flow simulation.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer From zero to turbulence: Generative modeling for 3d flow simulation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.490624Z

Source-reported events for the cited work

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Observation e4c8ab30-c46b-4c12-9036-32dbe44df522 · outbound

This paper cites Deepmetahandles: Learning deformation meta-handles of 3d meshes with biharmonic coordinates.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Deepmetahandles: Learning deformation meta-handles of 3d meshes with biharmonic coordinates

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.479974Z

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Observation 8be4d3dd-260f-49bf-96f3-368f479ca4c4 · outbound

This paper cites One-2-3-45++: Fast single image to 3d objects with consistent multi-view generation and 3d diffusion.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer One-2-3-45++: Fast single image to 3d objects with consistent multi-view generation and 3d diffusion

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.468699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6b6c56d4-0ae1-4f07-b69a-0d06484d7feb · outbound

This paper cites Zero-1-to-3: Zero-shot one image to 3d object.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Zero-1-to-3: Zero-shot one image to 3d object

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.457174Z

Source-reported events for the cited work

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Observation a2bb155d-4192-4498-b8cd-ac2283e25d30 · outbound

This paper cites MeshDiffusion: Score-based Generative 3D Mesh Modeling.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer MeshDiffusion: Score-based Generative 3D Mesh Modeling

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-11T11:04:46.795503Z

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Observation 3ab931ec-759d-4494-9048-615444495bab · outbound

This paper cites Decoupled Weight Decay Regularization.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Decoupled Weight Decay Regularization

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-11T11:04:46.799542Z

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Observation 7ece79b1-d03b-459a-b7b8-0393f6ecd3e9 · outbound

This paper cites Reconstructing three-dimensional bluff body wake from sectional flow fields with convolutional neural networks.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Reconstructing three-dimensional bluff body wake from sectional flow fields with convolutional neural networks

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-08-11T11:04:47.027399Z

Source-reported events for the cited work

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Observation 9deaab19-017e-4d07-a861-a3f4a21667b5 · outbound

This paper cites Pc2: Projection- conditioned point cloud diffusion for single-image 3d reconstruction.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Pc2: Projection- conditioned point cloud diffusion for single-image 3d reconstruction

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.446290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d04b6681-4596-487a-9814-76ba9bab5fb3 · outbound

This paper cites Occupancy networks: Learning 3d reconstruction in function space.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Occupancy networks: Learning 3d reconstruction in function space

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-11T11:04:46.811292Z

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Observation 0d796652-2784-4ba4-92fd-517a766f2c00 · outbound

This paper cites Nerf: Representing scenes as neural radiance fields for view synthesis.Communications of the ACM, 65(1):99–106, 2021.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Nerf: Representing scenes as neural radiance fields for view synthesis.Communications of the ACM, 65(1):99–106, 2021

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-11T11:04:46.814936Z

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Observation f9f7182f-144b-47cf-95f0-1f112aa5cb86 · outbound

This paper cites Dit-3d: Exploring plain diffusion transformers for 3d shape generation.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Dit-3d: Exploring plain diffusion transformers for 3d shape generation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.422493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8064d8fa-1738-493d-861b-faf1a16a8d7f · outbound

This paper cites Multidiff: Consistent novel view synthesis from a single image.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Multidiff: Consistent novel view synthesis from a single image

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-11T11:04:46.822088Z

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Observation 75171001-e023-4126-be29-74455d952ef4 · outbound

This paper cites Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow.Physics of Fluids, 33(2), 2021.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow.Physics of Fluids, 33(2), 2021

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.405341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6c250dd4-839e-4675-84d1-dff68bdab690 · outbound

This paper cites Point-E: A System for Generating 3D Point Clouds from Complex Prompts.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Point-E: A System for Generating 3D Point Clouds from Complex Prompts

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-11T11:04:46.829675Z

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Observation dcbefbe9-91e5-420b-bf1d-a0d1795d93fc · outbound

This paper cites RadSplat: Radiance Field-Informed Gaussian Splatting for Robust Real-Time Rendering with 900+ FPS.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer RadSplat: Radiance Field-Informed Gaussian Splatting for Robust Real-Time Rendering with 900+ FPS

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-11T11:04:46.833635Z

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Observation 0a112d2e-ccb5-41eb-a693-d1bfe19a9848 · outbound

This paper cites Cfdnet: A deep learning-based accelerator for fluid simulations.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Cfdnet: A deep learning-based accelerator for fluid simulations

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.394582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ddf42333-a7cd-4b76-9888-114304c128cb · outbound

This paper cites Scalable diffusion models with transformers.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Scalable diffusion models with transformers

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-11T11:04:46.841661Z

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Observation 89d853bf-0fd0-44b6-9266-1af84419e57d · outbound

This paper cites Turbulent flows.Measurement Science and Technology, 12(11): 2020–2021, 2001.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Turbulent flows.Measurement Science and Technology, 12(11): 2020–2021, 2001

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.377153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8db0a826-3bed-4dc8-92d6-a048d1deb67c · outbound

This paper cites an unresolved cited work.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-08-11T11:04:47.366654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 368674b3-584e-48fb-9b9b-37ccee0a2ac2 · outbound

This paper cites Acceleration of a full-scale industrial cfd application with op2.IEEE Transactions on Parallel and Distributed Systems, 27 (5):1265–1278, 2015.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Acceleration of a full-scale industrial cfd application with op2.IEEE Transactions on Parallel and Distributed Systems, 27 (5):1265–1278, 2015

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.355173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 54618c7c-635d-470b-a4a4-15533470600d · outbound

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

Reconstructing 3D Flow from 2D Data with Diffusion Transformer High-resolution image synthesis with latent diffusion models

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.342973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1827f084-c8b5-46d5-8bdb-f3bd76bd6ee6 · outbound

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

Reconstructing 3D Flow from 2D Data with Diffusion Transformer U-net: Convolutional networks for biomedical image segmentation

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-11T11:04:46.859082Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e3d2abf-eb4e-4f49-bead-78bbb973f7db · outbound

This paper cites Clip-forge: Towards zero-shot text-to- shape generation.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Clip-forge: Towards zero-shot text-to- shape generation

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.325315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2654e088-26b6-4402-a497-13e59308f55d · outbound

This paper cites Tomographic piv: principles and practice.Measurement Science and Technology, 24(1):012001, 2012.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Tomographic piv: principles and practice.Measurement Science and Technology, 24(1):012001, 2012

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.314539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b3681520-6847-4b47-aecc-379fa98bcd10 · outbound

This paper cites Shake-the-box: Lagrangian particle tracking at high particle image densities.Experiments in fluids, 57:1–27, 2016.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Shake-the-box: Lagrangian particle tracking at high particle image densities.Experiments in fluids, 57:1–27, 2016

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.303242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 269356dc-8b19-4409-81a3-d3097a86e8e7 · outbound

This paper cites Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-11T11:04:46.873007Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6807945a-cf2d-4204-941d-9f8acd56b3e0 · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Deep unsupervised learning using nonequilibrium thermodynamics

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.292219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9188a29b-bf68-4fff-990a-ae82766047f3 · outbound

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

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Score-Based Generative Modeling through Stochastic Differential Equations

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-11T11:04:46.880421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fafe20ad-a10a-414a-a444-2593951379fa · outbound

This paper cites Learned coarse models for efficient turbulence simulation.arXiv e-prints, pages arXiv–2112, 2021.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Learned coarse models for efficient turbulence simulation.arXiv e-prints, pages arXiv–2112, 2021

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.280838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1476ccee-dd71-4c15-994e-e9667eabcdc1 · outbound

This paper cites Pdebench: An exten- sive benchmark for scientific machine learning.Advances in Neural Information Processing Systems, 35:1596–1611, 2022.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Pdebench: An exten- sive benchmark for scientific machine learning.Advances in Neural Information Processing Systems, 35:1596–1611, 2022

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.268682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 27d05687-f4c3-48c3-8253-720781a2c09b · outbound

This paper cites Sa- convonet: Sign-agnostic optimization of convolutional occupancy networks.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Sa- convonet: Sign-agnostic optimization of convolutional occupancy networks

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.257615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 06009088-6564-45eb-9337-e1c6660c850f · outbound

This paper cites Accelerating eulerian fluid simulation with convolutional networks.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Accelerating eulerian fluid simulation with convolutional networks

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.246695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6ccf4227-63df-42a5-8c0e-8e96a2678aab · outbound

This paper cites Factorized fourier neural operators.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Factorized fourier neural operators

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.235590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8f1a2eab-f354-4c5d-9497-2eb8ba45bec9 · outbound

This paper cites Patchnets: Patch-based generalizable deep implicit 3d shape representations.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Patchnets: Patch-based generalizable deep implicit 3d shape representations

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.225110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1c13384d-10d5-4708-a056-e8c363747228 · outbound

This paper cites Lion: Latent point diffusion models for 3d shape generation.Advances in Neural Information Processing Systems, 35:10021–10039, 2022.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Lion: Latent point diffusion models for 3d shape generation.Advances in Neural Information Processing Systems, 35:10021–10039, 2022

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-11T11:04:46.905235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e50dcc2-50aa-4336-8b8d-384f8e66d2c2 · outbound

This paper cites Attention is all you need.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Attention is all you need

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-11T11:04:46.908805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e06674ad-9f61-490a-94ce-d036a7a37e42 · outbound

This paper cites Enhancing computational fluid dynamics with machine learning.Nature Computational Science, 2(6):358–366, 2022.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Enhancing computational fluid dynamics with machine learning.Nature Computational Science, 2(6):358–366, 2022

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-11T11:04:46.912273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd2fb625-0647-41eb-a221-6b43b3a1ca9e · outbound

This paper cites The transformative potential of machine learning for experiments in fluid mechanics.Nature Reviews Physics, 5(9):536–545, 2023.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer The transformative potential of machine learning for experiments in fluid mechanics.Nature Reviews Physics, 5(9):536–545, 2023

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.195553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 159b82c2-37cf-4baf-8670-f36657128574 · outbound

This paper cites SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image using Latent Video Diffusion.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image using Latent Video Diffusion

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-11T11:04:46.919098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4df4a23d-80d8-48e6-8ab4-eab7c20ebbda · outbound

This paper cites Learning incompressible fluid dynamics from scratch-towards fast, differentiable fluid models that generalize.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Learning incompressible fluid dynamics from scratch-towards fast, differentiable fluid models that generalize

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.185009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a1d1cbe8-3002-4390-89dc-9769f6a73c7b · outbound

This paper cites Reduced-order modeling for turbulent wake of a finite wall-mounted square cylinder based on artificial neural network.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Reduced-order modeling for turbulent wake of a finite wall-mounted square cylinder based on artificial neural network

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.173685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1f70f947-90b3-4ce0-8018-844d578d4224 · outbound

This paper cites A deep-learning approach for reconstructing 3d turbulent flows from 2d observation data.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer A deep-learning approach for reconstructing 3d turbulent flows from 2d observation data

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.163061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a415c459-6f8b-45ca-a3d5-99218253f1de · outbound

This paper cites Mip-splatting: Alias-free 3d gaussian splatting.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer Mip-splatting: Alias-free 3d gaussian splatting

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-11T11:04:46.933816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bcd5efff-19fb-49fa-ad38-0e9a8b86aa8a · outbound

This paper cites 3dilg: Irregular latent grids for 3d generative modeling.Advances in Neural Information Processing Systems, 35: 21871–21885, 2022.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer 3dilg: Irregular latent grids for 3d generative modeling.Advances in Neural Information Processing Systems, 35: 21871–21885, 2022

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.146043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5707a233-7292-4293-8d38-893353e74b2d · outbound

This paper cites 3dshape2vecset: A 3d shape representation for neural fields and generative diffusion models.ACM Transactions on Graphics (TOG), 42(4):1–16, 2023.

Reconstructing 3D Flow from 2D Data with Diffusion Transformer 3dshape2vecset: A 3d shape representation for neural fields and generative diffusion models.ACM Transactions on Graphics (TOG), 42(4):1–16, 2023

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:04:47.133958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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