Paper Citation Record · LEDGER

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction

As of 23 August 2026, this Paper Citation Record lists 66 of 66 outbound references and 2 inbound Pith citation observations for arXiv:2509.08752.

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

pith.paper-citation-record.v1
2509.08752 v1

Coverage vector

measured 66 of 66 reference resolution

Typed states for the displayed outbound observations.

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Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T10:03:45.002912Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T21:56:15.369801Z

Reference resolution

66 of 66 outbound references displayed

verified exact2
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External citation measurements

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Outbound references

Observation 1e5feb50-147a-4525-8618-210b15beb1f0 · outbound

This paper cites Critique of numerical modeling of fluid-mechanics phenomena.Annual Review of Fluid Mechanics, 2(1):15–36, 1970.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Critique of numerical modeling of fluid-mechanics phenomena.Annual Review of Fluid Mechanics, 2(1):15–36, 1970

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:13:10.785792Z

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Observation 9725357f-8433-4339-bb97-1dae09a9940c · outbound

This paper cites Numerical simulation of three-dimensional homogeneous isotropic turbulence.Physical review letters, 28(2):76, 1972.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Numerical simulation of three-dimensional homogeneous isotropic turbulence.Physical review letters, 28(2):76, 1972

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:13:10.774597Z

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Observation 3cc92b31-cd9c-4397-9069-dc937538a03c · outbound

This paper cites Spectral methods for problems in complex geometrics.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Spectral methods for problems in complex geometrics

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:13:10.763508Z

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Observation d263d02c-dd5a-47d5-8a60-6832c303861c · outbound

This paper cites Nodes, modes and flow codes.Physics Today, 46(3):34–42, 1993.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Nodes, modes and flow codes.Physics Today, 46(3):34–42, 1993

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:13:10.751566Z

Source-reported events for the cited work

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Observation 084e0dc0-dcfd-4f10-a046-e20d271e02c8 · outbound

This paper cites Learning nonlinear operators via deeponet based on the universal approximation theorem of operators.Nature machine intelligence, 3(3):218–229, 2021.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Learning nonlinear operators via deeponet based on the universal approximation theorem of operators.Nature machine intelligence, 3(3):218–229, 2021

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.606705Z

Source-reported events for the cited work

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Observation 1d4344a9-0f35-48ef-833a-0922bd4689ba · outbound

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

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Fourier Neural Operator for Parametric Partial Differential Equations

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.610506Z

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Observation a460e8b0-d07c-49fe-9e18-0d11476e6731 · outbound

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Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Unresolved cited work

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.614624Z

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Observation 61cd16ce-2893-4e4d-90cb-d5cf92cb6958 · outbound

This paper cites Laplace neural operator for solving differential equations.Nature Machine Intelligence, 6(6):631–640, 2024.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Laplace neural operator for solving differential equations.Nature Machine Intelligence, 6(6):631–640, 2024

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.618274Z

Source-reported events for the cited work

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Observation 5b4b4841-9077-4856-98a8-f8e52f5cb60d · outbound

This paper cites Convolutional neural operators.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Convolutional neural operators

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:13:10.720735Z

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Observation c4075e12-2348-4cc1-a02f-e8174a18fa43 · outbound

This paper cites On the spectral bias of neural networks.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction On the spectral bias of neural networks

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.625514Z

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Observation fd966d45-e86b-4251-90d8-40d52ebb06cf · outbound

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Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-15T16:13:10.702968Z

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Observation 05e5728b-af5d-48ef-8164-9984289c9e6f · outbound

This paper cites Towards Multi-spatiotemporal-scale Generalized PDE Modeling.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Towards Multi-spatiotemporal-scale Generalized PDE Modeling

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.633401Z

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Observation 8ec4c576-006d-43cc-b9f9-a522254c3ea1 · outbound

This paper cites Integrating neural operators with diffusion models improves spectral representation in turbulence modelling.Proceedings of the Royal Society A, 481(2309):20240819, 2025.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Integrating neural operators with diffusion models improves spectral representation in turbulence modelling.Proceedings of the Royal Society A, 481(2309):20240819, 2025

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:13:10.691995Z

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Observation 35b32628-d291-4de4-bfb1-85c6b7b6ee8f · outbound

This paper cites On understanding and overcoming spectral biases of deep neural network learning methods for solving pdes.Journal of Computational Physics, page 113905, 2025.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction On understanding and overcoming spectral biases of deep neural network learning methods for solving pdes.Journal of Computational Physics, page 113905, 2025

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:13:10.680709Z

Source-reported events for the cited work

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Observation 315ee48d-38eb-4ff6-9eaf-49a9f04c5ec1 · outbound

This paper cites MscaleFNO: Multi-scale Fourier Neural Operator Learning for Oscillatory Function Spaces.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction MscaleFNO: Multi-scale Fourier Neural Operator Learning for Oscillatory Function Spaces

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.644803Z

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Observation 1a677167-d29d-427d-bf77-ab841441768c · outbound

This paper cites Binned spectral power loss for improved prediction of chaotic systems.arXiv preprint arXiv:2502.00472, 2025.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Binned spectral power loss for improved prediction of chaotic systems.arXiv preprint arXiv:2502.00472, 2025

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.648796Z

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Observation aaf3cec3-1ea1-4403-9727-69e73aaf71fe · outbound

This paper cites Mitigating Spectral Bias in Neural Operators via High-Frequency Scaling for Physical Systems.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Mitigating Spectral Bias in Neural Operators via High-Frequency Scaling for Physical Systems

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.652109Z

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Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Unresolved cited work

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.655592Z

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Observation cc399080-bb61-462c-bf68-ca95a68e3519 · outbound

This paper cites From pinns to pikans: Recent advances in physics-informed machine learning.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction From pinns to pikans: Recent advances in physics-informed machine learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:13:10.662391Z

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Observation 77fb5d97-b703-4245-814c-b6ade382fcea · outbound

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Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Unresolved cited work

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.663201Z

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Observation 214a2149-2024-45e8-88a7-28e8558f67fa · outbound

This paper cites Piratenets: Physics-informed deep learning with residual adaptive networks.Journal of Machine Learning Research, 25(402):1–51, 2024.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Piratenets: Physics-informed deep learning with residual adaptive networks.Journal of Machine Learning Research, 25(402):1–51, 2024

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.667073Z

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Observation 945a691f-9d23-4fa9-bba1-bfd711f53730 · outbound

This paper cites Gradient alignment in physics-informed neural networks: A second-order optimization perspective.arXiv preprint arXiv:2502.00604, 2025.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Gradient alignment in physics-informed neural networks: A second-order optimization perspective.arXiv preprint arXiv:2502.00604, 2025

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.670580Z

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Observation 6b5e0fe0-bd8a-465c-9cf2-0faa3daec888 · outbound

This paper cites Simulating three-dimensional turbulence with physics-informed neural networks.arXiv preprint arXiv:2507.08972, 2025.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Simulating three-dimensional turbulence with physics-informed neural networks.arXiv preprint arXiv:2507.08972, 2025

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.674322Z

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Observation 43964b45-9a8c-4f77-99d8-c3591f40a06a · outbound

This paper cites Generative adversarial nets.Advances in neural information processing systems, 27, 2014.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Generative adversarial nets.Advances in neural information processing systems, 27, 2014

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.678531Z

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Observation 12e4f797-9fcd-4962-b3e7-13e54e62c778 · outbound

This paper cites Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.682088Z

Source-reported events for the cited work

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Observation 94d33385-3edd-48b9-8dba-73cbc574b622 · outbound

This paper cites Flow Matching for Generative Modeling.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Flow Matching for Generative Modeling

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.685997Z

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Observation 2f693c96-8366-43e6-b094-9ca4761c3835 · outbound

This paper cites Super-resolution reconstruction of turbulent flow fields at various reynolds numbers based on generative adversarial networks.Physics of Fluids, 34(1), 2022.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Super-resolution reconstruction of turbulent flow fields at various reynolds numbers based on generative adversarial networks.Physics of Fluids, 34(1), 2022

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:13:10.626564Z

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Observation 00fa9c1d-97c9-4cd1-b1c0-fd148f3cb3b8 · outbound

This paper cites Generative adversarial networks to infer velocity components in rotating turbulent flows.The European Physical Journal E, 46(5):31, 2023.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Generative adversarial networks to infer velocity components in rotating turbulent flows.The European Physical Journal E, 46(5):31, 2023

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:13:10.614864Z

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Observation cc733a35-946f-4dd3-964e-effa82ad4538 · outbound

This paper cites Influence of adversarial training on super-resolution turbulence reconstruction.Physical Review Fluids, 9(6):064601, 2024.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Influence of adversarial training on super-resolution turbulence reconstruction.Physical Review Fluids, 9(6):064601, 2024

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:13:10.603914Z

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Observation ee3d2885-dbea-4196-8462-71748b56ffd0 · outbound

This paper cites High-flexibility reconstruction of small-scale motions in wall turbulence using a generalized zero-shot learning.Journal of Fluid Mechanics, 990:R1, 2024.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction High-flexibility reconstruction of small-scale motions in wall turbulence using a generalized zero-shot learning.Journal of Fluid Mechanics, 990:R1, 2024

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:13:10.592689Z

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Observation 39936f19-531b-4e8c-85a6-93bbcc8db51e · outbound

This paper cites Three- dimensional generative adversarial networks for turbulent flow estimation from wall measurements.Journal of Fluid Mechanics, 991:A1, 2024.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Three- dimensional generative adversarial networks for turbulent flow estimation from wall measurements.Journal of Fluid Mechanics, 991:A1, 2024

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:13:10.581083Z

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Observation c0c3c691-f62e-400b-8bcf-a7ec5f4c5729 · outbound

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Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Unresolved cited work

Reference 32

Resolution: unresolved; raw_fallback, observed 2026-08-15T16:13:10.569787Z

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Observation e82ecd7a-cacf-4cfa-9f36-b82cdf6e75a2 · outbound

This paper cites A physics-informed diffusion model for high-fidelity flow field reconstruction.Journal of Computational Physics, 478:111972, 2023.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction A physics-informed diffusion model for high-fidelity flow field reconstruction.Journal of Computational Physics, 478:111972, 2023

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.714005Z

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Observation d00a4a95-12a0-4961-a6de-8885671d4244 · outbound

This paper cites Spectrally decomposed denoising diffusion probabilistic models for generative turbulence super-resolution.Physics of Fluids, 36(11), 2024.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Spectrally decomposed denoising diffusion probabilistic models for generative turbulence super-resolution.Physics of Fluids, 36(11), 2024

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:13:10.551553Z

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Observation 4cd520b0-7eb0-4520-959b-fb4cd8926e29 · outbound

This paper cites Conditional neural field latent diffusion model for generating spatiotemporal turbulence.Nature Communications, 15(1):10416, 2024.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Conditional neural field latent diffusion model for generating spatiotemporal turbulence.Nature Communications, 15(1):10416, 2024

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:13:10.540189Z

Source-reported events for the cited work

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Observation 91781b3d-0cd2-486f-81b4-6bbe8cd6856f · outbound

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

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Generative AI for fast and accurate statistical computation of fluids

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.725870Z

Source-reported events for the cited work

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Observation 8310bc0e-55e0-4db2-b1a3-653b7623ac07 · outbound

This paper cites Conditional flow matching for generative modeling of near-wall turbulence with quantified uncertainty.arXiv e-prints, pages arXiv–2504, 2025.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Conditional flow matching for generative modeling of near-wall turbulence with quantified uncertainty.arXiv e-prints, pages arXiv–2504, 2025

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:13:10.528498Z

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Observation 8afe7c1b-bf97-4fef-a7d2-d6fd6e6181d9 · outbound

This paper cites Diffusionpde: Generative pde-solving under partial observation.Advances in Neural Information Processing Systems, 37:130291–130323, 2024.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Diffusionpde: Generative pde-solving under partial observation.Advances in Neural Information Processing Systems, 37:130291–130323, 2024

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.733974Z

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Observation 4f09e0fd-04b9-4765-a275-3916e18b816f · outbound

This paper cites PolyMicros: Bootstrapping a Foundation Model for Polycrystalline Material Structure.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction PolyMicros: Bootstrapping a Foundation Model for Polycrystalline Material Structure

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.737847Z

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Observation b8ebd233-e7a3-444f-aa63-69d321f8435d · outbound

This paper cites Multimodal atmospheric super-resolution with deep generative models.arXiv preprint arXiv:2506.22780, 2025.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Multimodal atmospheric super-resolution with deep generative models.arXiv preprint arXiv:2506.22780, 2025

Reference 40

Resolution: verified exact; raw_fallback, observed 2026-08-15T16:13:10.079253Z

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Observation f8beced8-8423-471c-b853-a38ebc122e24 · outbound

This paper cites A review of recent developments in schlieren and shadowgraph techniques.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction A review of recent developments in schlieren and shadowgraph techniques

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.746404Z

Source-reported events for the cited work

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Observation 2de53f4a-caf7-4364-b625-45cd2da3046c · outbound

This paper cites Schlieren and bos velocimetry of a round turbulent helium jet in air.Optics and Lasers in Engineering, 156:107104, 2022.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Schlieren and bos velocimetry of a round turbulent helium jet in air.Optics and Lasers in Engineering, 156:107104, 2022

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:13:10.504917Z

Source-reported events for the cited work

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Observation be1589fc-05c4-4473-bf30-4fe511c48612 · outbound

This paper cites Deep-learning- based super-resolution reconstruction of high-speed imaging in fluids.Physics of Fluids, 34(3), 2022.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Deep-learning- based super-resolution reconstruction of high-speed imaging in fluids.Physics of Fluids, 34(3), 2022

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:13:10.492781Z

Source-reported events for the cited work

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Observation 117f1150-1b31-45a8-96fd-9460dcd84a38 · outbound

This paper cites Operator Learning for Reconstructing Flow Fields from Sparse Measurements: an Energy Transformer Approach.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Operator Learning for Reconstructing Flow Fields from Sparse Measurements: an Energy Transformer Approach

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.757526Z

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Observation 4c5ef64c-e53e-452d-9466-ff1b8e6510c5 · outbound

This paper cites Esrgan: Enhanced super-resolution generative adversarial networks.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Esrgan: Enhanced super-resolution generative adversarial networks

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.761495Z

Source-reported events for the cited work

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Observation 22b1d2cf-a69c-4eff-8d6d-03bf7da63ad4 · outbound

This paper cites Nekrs, a gpu-accelerated spectral element navier–stokes solver.Parallel Computing, 114:102982, 2022.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Nekrs, a gpu-accelerated spectral element navier–stokes solver.Parallel Computing, 114:102982, 2022

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:13:10.473870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3b1418ca-5c08-4dfc-a8b2-b72b6c2cebe3 · outbound

This paper cites The evolution of small-scale structures in homogeneous isotropic turbulence.Physics of Fluids A: Fluid Dynamics, 4(12):2747–2760, 1992.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction The evolution of small-scale structures in homogeneous isotropic turbulence.Physics of Fluids A: Fluid Dynamics, 4(12):2747–2760, 1992

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:13:10.462134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation dae7ae11-46a2-41dc-8f96-0c6a848997f9 · outbound

This paper cites Nektar++: Enhancing the capability and application of high-fidelity spectral/hp element methods.Computer Physics Communications, 249:107110, 2020.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Nektar++: Enhancing the capability and application of high-fidelity spectral/hp element methods.Computer Physics Communications, 249:107110, 2020

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:13:10.450186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 51693f99-4a34-48ce-82fe-6ad37ef75591 · outbound

This paper cites Aivt: Inference of turbulent thermal convection from measured 3d velocity data by physics-informed kolmogorov- arnold networks.Science advances, 11(19):eads5236, 2025.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Aivt: Inference of turbulent thermal convection from measured 3d velocity data by physics-informed kolmogorov- arnold networks.Science advances, 11(19):eads5236, 2025

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:13:10.439429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3db21893-4593-4d1c-a812-5080c654b9e6 · outbound

This paper cites Influence of static and dynamic downwash interactions on multi-quadrotor systems.arXiv preprint arXiv:2507.09463, 2025.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Influence of static and dynamic downwash interactions on multi-quadrotor systems.arXiv preprint arXiv:2507.09463, 2025

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Resolution: verified exact; raw_fallback, observed 2026-08-15T16:13:10.000594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c4cdb905-1eb3-44a8-af7e-4b75d96e8c2d · outbound

This paper cites Elucidating the design space of diffusion-based generative models.Advances in neural information processing systems, 35:26565–26577, 2022.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Elucidating the design space of diffusion-based generative models.Advances in neural information processing systems, 35:26565–26577, 2022

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.783424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9579af36-dd3b-4090-87cf-f1daa86a3a6f · outbound

This paper cites Real-esrgan: Training real-world blind super-resolution with pure synthetic data.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Real-esrgan: Training real-world blind super-resolution with pure synthetic data

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.786866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 973bbe87-dd3b-4dfb-a022-32e5b02d771e · outbound

This paper cites Aggregated contextual transformations for high-resolution image inpainting.IEEE transactions on visualization and computer graphics, 29(7):3266–3280, 2022.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Aggregated contextual transformations for high-resolution image inpainting.IEEE transactions on visualization and computer graphics, 29(7):3266–3280, 2022

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:13:10.416586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4d1f630c-c469-4dcb-bd5d-799dc43ae0e9 · outbound

This paper cites Cambridge University Press, 2007.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Cambridge University Press, 2007

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:13:10.405458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9178543e-a332-43b7-b2fc-30dfeff952fa · outbound

This paper cites Frequency principle: Fourier analysis sheds light on deep neural networks.Communications in Computational Physics, 28(5):1746–1767, 2020.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Frequency principle: Fourier analysis sheds light on deep neural networks.Communications in Computational Physics, 28(5):1746–1767, 2020

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.797686Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a731f93-c90c-4ba6-947c-429f3a336dd7 · outbound

This paper cites Implicit neural representations with periodic activation functions.Advances in neural information processing systems, 33:7462– 7473, 2020.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Implicit neural representations with periodic activation functions.Advances in neural information processing systems, 33:7462– 7473, 2020

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.802357Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2896fa2a-c8be-43bf-ba16-a23c221cd07a · outbound

This paper cites Towards the Spectral bias Alleviation by Normalizations in Coordinate Networks.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Towards the Spectral bias Alleviation by Normalizations in Coordinate Networks

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.805384Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bda03545-ea64-451e-961d-c52878608fa7 · outbound

This paper cites On spectral bias reduction of multi-scale neural networks for regression problems.Neural Networks, 185:107179, 2025.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction On spectral bias reduction of multi-scale neural networks for regression problems.Neural Networks, 185:107179, 2025

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:13:10.381082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 76223e1f-b2db-4039-8968-abbb5431768f · outbound

This paper cites The relativistic discriminator: a key element missing from standard GAN.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction The relativistic discriminator: a key element missing from standard GAN

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.812586Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6efac844-622c-4920-9174-5780746bf22b · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.816061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf4c5858-a964-4a25-bb66-1f8e37fa8712 · outbound

This paper cites Med3D: Transfer Learning for 3D Medical Image Analysis.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Med3D: Transfer Learning for 3D Medical Image Analysis

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.819862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0b8dbf9-761d-453c-85bf-7ab84c346fd4 · outbound

This paper cites Neural discrete representation learning.Advances in neural information processing systems, 30, 2017.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Neural discrete representation learning.Advances in neural information processing systems, 30, 2017

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.823224Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e24e6fd-e045-41a2-b0c5-2d82509c677f · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Adam: A Method for Stochastic Optimization

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.826936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55b07618-cea6-4ca1-a00e-53e660615176 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.Advances in neural information processing systems, 32, 2019.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Pytorch: An imperative style, high-performance deep learning library.Advances in neural information processing systems, 32, 2019

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-15T16:13:09.830819Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13b6b559-fa4d-4dde-bad0-1c62c665e98b · outbound

This paper cites Karniadakis.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Karniadakis

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:13:10.354761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a1ce22d9-7ef0-498b-b8af-b62f5e5445d2 · outbound

This paper cites Mémoire sur les séries et sur l’intégration complète d’une équation aux différences partielles linéaires du second ordre, à coefficients constants.Mém.

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction Mémoire sur les séries et sur l’intégration complète d’une équation aux différences partielles linéaires du second ordre, à coefficients constants.Mém

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:13:10.342433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

Observation 06de3502-061b-48cb-b82c-6092dab62f40 · inbound

GenDA: Generative Data Assimilation on Complex Urban Areas via Classifier-Free Diffusion Guidance cites this paper.

GenDA: Generative Data Assimilation on Complex Urban Areas via Classifier-Free Diffusion Guidance Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T10:03:45.002912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3223bebe-d9d8-42c4-ace4-2df0d279f72a · inbound

Emergent Transfer of a Physics Foundation Model from Simulation to Laboratory Turbulence cites this paper.

Emergent Transfer of a Physics Foundation Model from Simulation to Laboratory Turbulence Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction

Reference 70

Resolution: verified exact; arxiv_id, observed 2026-07-01T21:56:15.371516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.