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Paper Citation Record · LEDGER

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations

As of 9 August 2026, this Paper Citation Record lists 64 of 64 outbound references and 0 inbound Pith citation observations for arXiv:2510.07314.

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

pith.paper-citation-record.v1
2510.07314 v3

Coverage vector

measured 64 of 64 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T11:03:19.564756Z

measured 64 of 64 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

64 of 64 outbound references displayed

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  • verified fuzzy0
  • unresolved48
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  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f8f83b1e-065f-4200-abe4-05a651672938 · outbound

This paper cites Accurate structure prediction of biomolecular interactions with alphafold 3.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Accurate structure prediction of biomolecular interactions with alphafold 3

Reference 1

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Observation 005aabe8-f000-46df-b1e5-58336bfa3ca7 · outbound

This paper cites Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators

Reference 2

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Observation 9196e744-243c-45b0-9903-f82e3de1d38a · outbound

This paper cites NeuralDEM -- Real-time Simulation of Industrial Particulate Flows.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations NeuralDEM -- Real-time Simulation of Industrial Particulate Flows

Reference 3

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Observation ec44cfe7-49d2-4a80-ab78-83cbd235713f · outbound

This paper cites Accurate medium-range global weather forecasting with 3d neural networks.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Accurate medium-range global weather forecasting with 3d neural networks

Reference 4

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Observation 0a38f195-be4d-4959-85fa-2063368ed3e2 · outbound

This paper cites an unresolved cited work.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Unresolved cited work

Reference 5

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Observation c397b7d9-30fe-4e97-bc37-47edd882d7b8 · outbound

This paper cites A Foundation Model for the Earth System.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations A Foundation Model for the Earth System

Reference 6

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Observation a12b4a3c-e718-49c5-bb2a-72cc4a81ba78 · outbound

This paper cites Bourdelle, X.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Bourdelle, X

Reference 7

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Observation 8acea66c-62de-42b8-aa8c-c0ff49223b57 · outbound

This paper cites Bourdelle, A.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Bourdelle, A

Reference 8

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Observation c75b5117-521a-4bfd-87a2-c2b966061c5d · outbound

This paper cites Core turbulent transport in tokamak plasmas: bridging theory and experiment with QuaLiKiz.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Core turbulent transport in tokamak plasmas: bridging theory and experiment with QuaLiKiz

Reference 9

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 5b4016f3-451b-46ba-a39e-a176cd5aef51 · outbound

This paper cites Envisioning better benchmarks for machine learning pde solvers.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Envisioning better benchmarks for machine learning pde solvers

Reference 10

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Observation d74c43c9-3bec-4f7d-b30e-ddb69a4d6f92 · outbound

This paper cites Brunton, Bernd R.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Brunton, Bernd R

Reference 11

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Observation de1dfdfe-a2c4-461a-9293-9e3f37e069a3 · outbound

This paper cites o rler, O G\.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations o rler, O G\

Reference 12

Resolution
verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 31985a60-4336-4e65-85f8-68b4bd12b974 · outbound

This paper cites Citrin, P.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Citrin, P

Reference 13

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation fe4e656c-434a-4c75-9566-bc79498e9b17 · outbound

This paper cites Torax: A fast and differentiable tokamak transport simulator in jax, 2024.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Torax: A fast and differentiable tokamak transport simulator in jax, 2024

Reference 14

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Observation b267af91-9b3a-4eff-85c8-d58d36bfe90a · outbound

This paper cites an unresolved cited work.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Unresolved cited work

Reference 15

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Observation dbbd747f-2c8a-4aa2-ad02-e2b575611940 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations An image is worth 16x16 words: Transformers for image recognition at scale

Reference 16

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Observation 48191b18-1ce8-4de3-975e-21bf6a53ea23 · outbound

This paper cites Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position

Reference 17

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Observation 8e468037-5d94-40c4-bdcc-a99e034cf6b6 · outbound

This paper cites Multimodal convolutional neural networks for predicting evolution of gyrokinetic simulations.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Multimodal convolutional neural networks for predicting evolution of gyrokinetic simulations

Reference 18

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation f0d03acd-1004-4ddc-95fc-ddc6a609b4d4 · outbound

This paper cites A Hornsby, A.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations A Hornsby, A

Reference 19

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 9c715a27-349d-4b0d-89f1-8256bd9b452e · outbound

This paper cites Itoh, S.-I.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Itoh, S.-I

Reference 20

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation f79d216a-62b6-4ca6-b643-e5aa8c7709a1 · outbound

This paper cites Perceiver: General perception with iterative attention.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Perceiver: General perception with iterative attention

Reference 21

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Observation 078dc5bd-713c-4b98-b9c5-d623b113865a · outbound

This paper cites Highly accurate protein structure prediction with alphafold.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Highly accurate protein structure prediction with alphafold

Reference 22

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Observation 8fdd3d91-0c5b-4e06-a7a7-0e075246e494 · outbound

This paper cites Kiefer, C.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Kiefer, C

Reference 23

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 8537cb4b-cff6-487e-9f9f-ea97024645ad · outbound

This paper cites Swift: Swin 4d fmri transformer.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Swift: Swin 4d fmri transformer

Reference 24

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Observation 967110c0-fc0a-43ff-a262-bf99a551f7a0 · outbound

This paper cites Kingma and Jimmy Ba.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Kingma and Jimmy Ba

Reference 25

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Observation ed17f2d8-7299-41ed-a634-cd3ac615841c · outbound

This paper cites Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew M.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew M

Reference 26

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Observation a079683f-c118-4dd9-b287-d55d21d2efae · outbound

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GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Unresolved cited work

Reference 27

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Observation ac8cc4e2-2b46-4d00-933f-fceb818c1883 · outbound

This paper cites Kumar, Y.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Kumar, Y

Reference 28

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Observation 5e071f7c-387b-49bb-9aad-4beb8b812a83 · outbound

This paper cites Fourcastnet: Accelerating global high-resolution weather forecasting using adaptive fourier neural operators.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Fourcastnet: Accelerating global high-resolution weather forecasting using adaptive fourier neural operators

Reference 29

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Observation 2f20e3b7-e298-4290-8f2f-3b3dc867f632 · outbound

This paper cites Learning skillful medium-range global weather forecasting.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Learning skillful medium-range global weather forecasting

Reference 30

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Observation 099881d2-8cad-4b89-aec6-5d2b908d6681 · outbound

This paper cites Boser, John S.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Boser, John S

Reference 31

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Observation fae126f7-f4dc-4589-9c9d-3f981340a273 · outbound

This paper cites Neural Operator: Graph Kernel Network for Partial Differential Equations.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Neural Operator: Graph Kernel Network for Partial Differential Equations

Reference 32

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Observation 75988ee6-1abf-4394-81d8-bf7abc963ddf · outbound

This paper cites Fourier neural operator for parametric partial differential equations, 2021.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Fourier neural operator for parametric partial differential equations, 2021

Reference 33

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Observation af39f544-4619-4c81-bfa0-5a4c08a78f97 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Swin transformer: Hierarchical vision transformer using shifted windows

Reference 34

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Observation 30c5eb4f-c7c7-4cef-8907-d897e1e6d79a · outbound

This paper cites Video swin transformer.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Video swin transformer

Reference 35

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Observation 6ce9919d-8f9d-4021-a164-20823485459c · outbound

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GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Unresolved cited work

Reference 36

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Observation 34f70319-e64f-4e4c-8980-d91ca2850890 · outbound

This paper cites Batzner, Samuel S.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Batzner, Samuel S

Reference 37

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-04T11:03:17.169299Z digest=sha256:9d73981a17d97effd1aae0bf455abae90183f3b3eea0c10aa54792d4c3e1927d

Observation 4cb39d9b-8df1-423f-b4cc-7fe60c9e4e45 · outbound

This paper cites Van Mulders, F.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Van Mulders, F

Reference 38

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no resolver link, observed 2026-08-04T11:03:17.327556Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T11:03:17.327556Z digest=sha256:2bafef7c01b084b16ee90dfb9f62f85f80d5a7f55401c20e97eb3009b272cad7

Observation c0f9e2a1-7863-4814-9b1e-bc039e5ed690 · outbound

This paper cites Narita, M.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Narita, M

Reference 39

Resolution
verified exact
doi, observed 2026-08-04T11:04:39.580319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-04T11:03:17.397736Z digest=sha256:2c54df240e8c7931dbcc18f3e42fbc0eeb3bdce5630e54572602ff1e6e3fe1e4

Observation a8ef3ffd-ff1b-479c-b4b5-12c888f6d202 · outbound

This paper cites Gupta, and Aditya Grover.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Gupta, and Aditya Grover

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-04T11:03:17.475960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T11:03:17.475960Z digest=sha256:696d2b90e72ccf6148c20342936f2a91069f49cfdd2bc159fb87dc3ca043a1a8

Observation 132b14a4-5f80-48da-bd7b-cf5f96c3e8b0 · outbound

This paper cites Scalable diffusion models with transformers.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Scalable diffusion models with transformers

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-04T11:03:17.564412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T11:03:17.564412Z digest=sha256:49729edd5dc0827ea43f698da5bb4ec2239256836b9fe59685941a184632b8a2

Observation 6b2e14bb-3213-4085-82b7-ff00902dbfab · outbound

This paper cites Peeters, Y.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Peeters, Y

Reference 42

Resolution
verified exact
doi, observed 2026-08-04T11:04:39.145819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-04T11:03:17.714201Z digest=sha256:12f5051b37856e1d1987e948e6083581306214f9336ce9ac3c5475446b2cd0e4

Observation f1c5edc0-afdf-4edc-8882-b9ac3e32382e · outbound

This paper cites Courville.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Courville

Reference 43

Resolution
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no resolver link, observed 2026-08-04T11:03:17.796329Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T11:03:17.796329Z digest=sha256:20265503632f291bf4168bbff4aed43a249139bea03455c25f814ed08adcb038

Observation 2973344e-5d32-4c39-9d3b-7ca19f13a345 · outbound

This paper cites PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-04T11:03:17.827209Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T11:03:17.827209Z digest=sha256:e5f90fb28f01ec57247aac52dfbc469c5d13279c86e4e05380a33e64235bdf01

Observation a62f83d4-974b-4df0-8055-ccd3b06e5690 · outbound

This paper cites Hamprecht, Yoshua Bengio, and Aaron C.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Hamprecht, Yoshua Bengio, and Aaron C

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-04T11:03:17.887876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T11:03:17.887876Z digest=sha256:defb4e70e0c937386ce5b534285975156a9bfc4666c68e3428a9c1815ba0b6b2

Observation 73c5c4f0-974c-47a5-bfa9-81f75490640d · outbound

This paper cites an unresolved cited work.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Unresolved cited work

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-04T11:03:17.956317Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T11:03:17.956317Z digest=sha256:fd25fcbfe6d40fe5a03820d6f6dbb442ecccb69f185a736fa51c40b437ddc7b7

Observation 7b650ed5-45aa-4a6d-998f-b229ce26560a · outbound

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

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations U-net: Convolutional networks for biomedical image segmentation

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-04T11:03:18.005584Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T11:03:18.005584Z digest=sha256:2a75aa817c474cf30599ba0b967f6f4530313d69a0b50da5c36aad57f8faa643

Observation 884474d6-61c5-4d3a-a938-8bb17299fce0 · outbound

This paper cites Simshift: A benchmark for adapting neural surrogates to distribution shifts, 2025.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Simshift: A benchmark for adapting neural surrogates to distribution shifts, 2025

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-04T11:03:18.075426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T11:03:18.075426Z digest=sha256:edc1abe5c306407e70c2e46720ecd120b105ab63df376c35d54459da286dc233

Observation 26b06a67-c343-41e9-8103-dea6a8955703 · outbound

This paper cites Staebler, C.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Staebler, C

Reference 49

Resolution
verified exact
doi, observed 2026-08-04T11:04:38.738333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-04T11:03:18.203385Z digest=sha256:f32d39d975e0087ebe6e15f6aed2ee69f5087da39ceac6309f4b0b1f2b920d75

Observation 15d1443d-a9a6-4eaa-92ad-764e57a96db6 · outbound

This paper cites an unresolved cited work.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Unresolved cited work

Reference 50

Resolution
verified exact
doi, observed 2026-08-04T11:04:38.407162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-04T11:03:18.302395Z digest=sha256:75e63491b47702d7d81b67b9ac3675495cb2ae4a6b50fad2968c61efa1e4ad83

Observation 8f215199-781f-4721-bc86-dc7a178bffd3 · outbound

This paper cites an unresolved cited work.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Unresolved cited work

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-04T11:03:18.366756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T11:03:18.366756Z digest=sha256:cd1f6e840732a207ff352db6002b1b1aefa4b94a0f86acac00e77db317addb99

Observation 19da7bb8-30be-4a4a-ace6-2183152e67db · outbound

This paper cites Physics-based Deep Learning.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Physics-based Deep Learning

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-04T11:03:18.434025Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T11:03:18.434025Z digest=sha256:2fd1bb6c56ab4e55ff558761bb2c08d9c1b31dbef8829d2f7c6f6fa90c534df3

Observation 8a3b408c-ccec-44d4-a87b-f6bd79c5f81c · outbound

This paper cites Factorized fourier neural operators, 2023.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Factorized fourier neural operators, 2023

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-04T11:03:18.478771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T11:03:18.478771Z digest=sha256:ff43e7176d7771085766c6e12149d12990e9343dcb294b5270a289e6ce12cfb1

Observation 5b6ff1b8-a625-4eb4-8c33-1af9fb44c7a2 · outbound

This paper cites The Particle-in-Cell Method, pp.\ 161--189.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations The Particle-in-Cell Method, pp.\ 161--189

Reference 54

Resolution
verified exact
doi, observed 2026-08-04T11:04:38.111190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-04T11:03:18.544740Z digest=sha256:d8f6d3c48f38a26056bb64327df8854457794c9a5b88b7d9d56c626e87ae3c4e

Observation 314b2729-ac8a-4159-a531-91338a63ccf9 · outbound

This paper cites an unresolved cited work.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Unresolved cited work

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-04T11:03:18.624748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T11:03:18.624748Z digest=sha256:0d5915de3df420795053fd62d5c91cc1cb2189689e8874770bde66b0eeed4bb5

Observation 9e13c927-ed30-4fbe-979a-f608d479fc51 · outbound

This paper cites Gomez, Lukasz Kaiser, and Illia Polosukhin.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Gomez, Lukasz Kaiser, and Illia Polosukhin

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-04T11:03:18.734878Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T11:03:18.734878Z digest=sha256:24d012a4551340338775285f1641271df73293cfa2e9f124e31c542f8cf9a3b2

Observation a189f968-5390-44b4-8ff0-1be726645646 · outbound

This paper cites A high-fidelity surrogate model for the ion temperature gradient (itg) instability using a small expensive simulation dataset.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations A high-fidelity surrogate model for the ion temperature gradient (itg) instability using a small expensive simulation dataset

Reference 57

Resolution
verified exact
doi, observed 2026-08-04T11:04:37.836719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-04T11:03:18.886979Z digest=sha256:fc7adb74b07d82855344ac6e3269a39a29fdc9ce3c89b4e915a96f39d639bdcd

Observation 30ab46f6-66e8-402c-bca2-460c2ec6ff34 · outbound

This paper cites Factorized convolutional neural networks.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Factorized convolutional neural networks

Reference 58

Resolution
verified exact
doi, observed 2026-08-04T11:04:37.497087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-04T11:03:18.964857Z digest=sha256:65af37ccf399cd2626f3ecade990c71b98f2dd363d86a19ddbd9cdcb0428327f

Observation 5d0ec3ab-d854-41f9-9de6-dae970e132ea · outbound

This paper cites Transolver: A fast transformer solver for pdes on general geometries.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Transolver: A fast transformer solver for pdes on general geometries

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-04T11:03:19.084788Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T11:03:19.084788Z digest=sha256:6c7649c37be7706ec22402b42cbd455b4bdef8a2fee9ac09cef426043ef001e0

Observation 6530b0a2-1b14-43df-ad78-d9a9ed2f8273 · outbound

This paper cites MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-04T11:03:19.214757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T11:03:19.214757Z digest=sha256:83cbdf44ac44476213ba11554c668b769f947342ef7acdd757e00edce073eb90

Observation b9d168c1-255d-4395-9e93-c5b49cb91622 · outbound

This paper cites Zanisi, A.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Zanisi, A

Reference 61

Resolution
verified exact
doi, observed 2026-08-04T11:04:37.249345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-04T11:03:19.324774Z digest=sha256:517ed67bb835b1a35de75c98d3a2a46cce520f22720b5d35fd8634c3ec7624f9

Observation 55864391-0242-4cfc-99cb-8d6b59edd1a5 · outbound

This paper cites A generative model for inorganic materials design.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations A generative model for inorganic materials design

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-04T11:03:19.404308Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T11:03:19.404308Z digest=sha256:7831b68ddd7b50c8f12bc9b9390e670b44b862f4058ea88b6ba5ff7b85c27070

Observation dbc17910-9755-49ab-a0f7-0011c940c1d6 · outbound

This paper cites Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

Reference 63

Resolution
unresolved
no resolver link, observed 2026-08-04T11:03:19.475533Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T11:03:19.475533Z digest=sha256:7408e003bba7f9fd51b93b5dd7e352100c22471487ea4bd0a3b362db36f6f221

Observation 8e3bed92-dba8-40ed-9e2b-acf7611c981c · outbound

This paper cites write newline.

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations write newline

Reference 64

Resolution
unresolved
no resolver link, observed 2026-08-04T11:03:19.564756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T11:03:19.564756Z digest=sha256:e9acee6fc79da5817b379443787e1051544223c2c105b07c9efb2b54d4532d14

Pith citing papers

No inbound Pith citation observations are available.