Paper Citation Record · LEDGER

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data

As of 8 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2505.21421.

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

pith.paper-citation-record.v1
2505.21421 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:40:20.808373Z

measured 44 of 44 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

44 of 44 outbound references displayed

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verified fuzzy25
unresolved15
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 21382136-fabf-46d6-ba12-1b4812156eac · outbound

This paper cites The graph neural network model,.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data The graph neural network model,

Reference 1

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Observation 007414b4-2bf7-4039-b0fd-abb9546674e3 · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Semi-Supervised Classification with Graph Convolutional Networks

Reference 2

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Observation dc7ee00b-17e7-491b-8039-72c140d44e90 · outbound

This paper cites Graph Attention Networks.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Graph Attention Networks

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T13:40:16.273662Z

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Observation 91248fc5-ffb4-447d-b87d-99a7ee3097be · outbound

This paper cites Inductive representation learning on large graphs,.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Inductive representation learning on large graphs,

Reference 4

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Observation 071657b6-65b2-4490-95c2-95e36e7051a5 · outbound

This paper cites How Powerful are Graph Neural Networks?.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data How Powerful are Graph Neural Networks?

Reference 5

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Observation 745669c9-33aa-407a-ae04-3fadae8287f7 · outbound

This paper cites Neural Machine Translation by Jointly Learning to Align and Translate.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Neural Machine Translation by Jointly Learning to Align and Translate

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T13:40:16.679018Z

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Observation a776580f-bfc4-4dc1-9e52-4d43086d5914 · outbound

This paper cites Effective Approaches to Attention-based Neural Machine Translation.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Effective Approaches to Attention-based Neural Machine Translation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T13:40:16.785808Z

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Observation 0beb26c2-9b36-46a3-bff7-80cad61ac262 · outbound

This paper cites Attention is all you need,.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Attention is all you need,

Reference 8

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Observation 3598bca3-b9e9-4e5a-8f4d-02e105a1a054 · outbound

This paper cites Improving language understanding by generative pre-training,.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Improving language understanding by generative pre-training,

Reference 9

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Observation 0f53eb42-e6f0-4f2f-a08a-ef755e598221 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding,.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Bert: Pre-training of deep bidirectional transformers for language understanding,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:25.864326Z

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Observation 785a48a7-eb38-4f57-8d44-0cd44c0695ec · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 11

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Observation 380334ae-ce3e-4139-9545-0f4207d78895 · outbound

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

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Transformers are rnns: Fast autoregressive transformers with linear attention,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:25.685853Z

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Observation e451ecb0-ae27-4287-89a3-4e52c9dda75d · outbound

This paper cites Rethinking Attention with Performers.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Rethinking Attention with Performers

Reference 13

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Observation 520164eb-aa8c-468b-9a29-7009d8295c58 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 14

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Observation 3c1544d7-81c5-4b7f-80c0-c4cffb42079f · outbound

This paper cites Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Reference 15

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Observation 9892ce18-e39f-4480-9436-a529f4f5574b · outbound

This paper cites A Generalization of Transformer Networks to Graphs.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data A Generalization of Transformer Networks to Graphs

Reference 16

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Observation 298bc1e2-dae9-4dbc-9ed4-dc6f8993862c · outbound

This paper cites Rethinking graph transformers with spectral attention,.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Rethinking graph transformers with spectral attention,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:25.494108Z

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Observation 4e47545b-1ae7-4131-a6e2-50f5b9e7e652 · outbound

This paper cites Do transformers really perform badly for graph representation?.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Do transformers really perform badly for graph representation?

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:25.335995Z

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Observation 10a6c058-9e34-4926-9234-7297c83e7a55 · outbound

This paper cites Graph mamba: Towards learning on graphs with state space models,.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Graph mamba: Towards learning on graphs with state space models,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:25.119785Z

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Observation ddc805cc-c38e-49f5-b375-d5107813675b · outbound

This paper cites Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Reference 20

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Observation 80a5f505-c1f3-4d0d-a996-34cd3a3ab18f · outbound

This paper cites Recipe for a general, powerful, scalable graph transformer,.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Recipe for a general, powerful, scalable graph transformer,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:24.949273Z

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Observation 30b2e35f-e0f9-4286-ae00-78bac62768ed · outbound

This paper cites On the unreasonable effectiveness of feature propagation in learning on graphs with missing node features,.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data On the unreasonable effectiveness of feature propagation in learning on graphs with missing node features,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:24.832967Z

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Observation d0072472-33da-4e5f-a656-01f59e3a4b8f · outbound

This paper cites Karhunen–loeve procedure for gappy data,.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Karhunen–loeve procedure for gappy data,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:24.725151Z

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Observation e747d329-12fd-4c17-a94d-a7bb293e3616 · outbound

This paper cites Aerodynamic data reconstruction and inverse design using proper orthogonal decomposition,.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Aerodynamic data reconstruction and inverse design using proper orthogonal decomposition,

Reference 24

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Observation 9b41b6bf-59da-4b76-bede-dcc05a908cd4 · outbound

This paper cites Proper orthogonal decomposition for steady aerodynamic applications,.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Proper orthogonal decomposition for steady aerodynamic applications,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:24.376521Z

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Observation 9fad9202-9118-401a-bc14-574784f795a6 · outbound

This paper cites A surrogate-based flow-field prediction and optimization strategy for hypersonic thrust nozzle,.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data A surrogate-based flow-field prediction and optimization strategy for hypersonic thrust nozzle,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:24.222362Z

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Observation 2667d021-75fd-467c-bb9c-16496437fddc · outbound

This paper cites Graph Neural Networks for Aerodynamic Flow Reconstruction from Sparse Sensing.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Graph Neural Networks for Aerodynamic Flow Reconstruction from Sparse Sensing

Reference 27

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Observation eb5daaa1-7410-4745-8910-3ca6a122b91b · outbound

This paper cites A practical approach to flow field reconstruction with sparse or incomplete data through physics informed neural network,.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data A practical approach to flow field reconstruction with sparse or incomplete data through physics informed neural network,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:24.050418Z

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Observation e2b859b2-3da9-46d9-9e74-51f65b60b5e6 · outbound

This paper cites Gappy ae: A nonlinear approach for gappy data reconstruction using auto-encoder,.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Gappy ae: A nonlinear approach for gappy data reconstruction using auto-encoder,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:23.794554Z

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Observation 1aaee1c6-c68e-4f43-a19e-e409f8d50509 · outbound

This paper cites Rapid and sparse reconstruction of high-speed steady-state and transient compressible flow fields using physics-informed graph neural networks,.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Rapid and sparse reconstruction of high-speed steady-state and transient compressible flow fields using physics-informed graph neural networks,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:23.613503Z

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Observation 72d6e7ac-b947-4ee6-a613-60cac8cd5c29 · outbound

This paper cites Flow field reconstruction from sparse sensor measurements with physics-informed neural networks,.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Flow field reconstruction from sparse sensor measurements with physics-informed neural networks,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:23.474078Z

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Observation 7172c0e7-5521-4221-a7f9-cea5e90b865c · outbound

This paper cites Flow reconstruction in time-varying geometries using graph neural networks.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Flow reconstruction in time-varying geometries using graph neural networks

Reference 32

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Observation bf64cc84-8626-4c16-8833-d1a5a1d73f4e · outbound

This paper cites Mean flow data assimilation using physics-constrained Graph Neural Networks.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Mean flow data assimilation using physics-constrained Graph Neural Networks

Reference 33

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Observation d59c9d8f-04e9-4f91-b3a8-58213e9ca627 · outbound

This paper cites FLRNet: A Deep Learning Method for Regressive Reconstruction of Flow Field From Limited Sensor Measurements.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data FLRNet: A Deep Learning Method for Regressive Reconstruction of Flow Field From Limited Sensor Measurements

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:40:21.360320Z

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Observation b275e1c0-072c-4050-a5bc-742d17189132 · outbound

This paper cites Flronet: Deep operator learning for high-fidelity fluid flow field reconstruction from sparse sensor measurements,.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Flronet: Deep operator learning for high-fidelity fluid flow field reconstruction from sparse sensor measurements,

Reference 35

Resolution: verified exact; raw_fallback, observed 2026-08-07T13:40:21.198662Z

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Observation 1f5d24b5-7d15-411a-8956-db0c6167c89a · outbound

This paper cites On the choice of wavespeeds for the HLLC riemann solver,.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data On the choice of wavespeeds for the HLLC riemann solver,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:23.337557Z

Source-reported events for the cited work

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Observation 5f84f0e2-ee73-4eb9-99cb-7195690d203b · outbound

This paper cites Classification of the Riemann problem for two-dimensional gas dynamics,.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Classification of the Riemann problem for two-dimensional gas dynamics,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:23.190509Z

Source-reported events for the cited work

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Observation 1684817d-336e-49fe-bdf1-85f5bb0c8b2c · outbound

This paper cites Asystematicanalysisofthree-dimensionalriemannproblemsforverification of compressible-flow solvers,.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Asystematicanalysisofthree-dimensionalriemannproblemsforverification of compressible-flow solvers,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:23.073469Z

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Observation 21189e54-8cd7-4336-ac3d-fac54408703b · outbound

This paper cites Benchmarking graph neural networks,.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Benchmarking graph neural networks,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:22.963595Z

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Observation d5860c99-c8e3-4e33-9b73-4d0ec8924ffb · outbound

This paper cites Exphormer: Sparse transformers for graphs,.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Exphormer: Sparse transformers for graphs,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:22.733604Z

Source-reported events for the cited work

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Observation 82b0a60e-45b4-4328-a9ee-5ac3ab814195 · outbound

This paper cites Godunov loss functions for modelling of hyperbolic conservation laws,.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Godunov loss functions for modelling of hyperbolic conservation laws,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:22.595151Z

Source-reported events for the cited work

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Observation 460586c7-0ea7-468a-aa76-66da7dc810fb · outbound

This paper cites The HLLC Riemann solver,.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data The HLLC Riemann solver,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:22.429679Z

Source-reported events for the cited work

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Observation a1f6eee5-da11-450b-aa93-fe1517516f15 · outbound

This paper cites Springer Science & Business Media, 2013.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Springer Science & Business Media, 2013

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:22.271294Z

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.

Observation 16083362-f89b-4c55-be7a-17a5c4f49728 · outbound

This paper cites Threedimensionalhllriemannsolverforconservationlawsonstructuredmeshes;applicationtoeulerandmagnetohydrodynamic flows,.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Threedimensionalhllriemannsolverforconservationlawsonstructuredmeshes;applicationtoeulerandmagnetohydrodynamic flows,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:22.131713Z

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.

Pith citing papers

No inbound Pith citation observations are available.