Paper Citation Record · LEDGER

Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2402.12365.

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

pith.paper-citation-record.v1
2402.12365 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T04:31:44.597510Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T13:43:29.094077Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation c0754892-73a0-4bea-8c2d-ea3b12de43f4 · inbound

Implicit factorized transformer approach to fast prediction of turbulent channel flows cites this paper.

Implicit factorized transformer approach to fast prediction of turbulent channel flows Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T04:31:44.597510Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f29dd85-b467-4165-adfb-04a6d4d8b49f · inbound

Neuro-Symbolic AI for Analytical Solutions of Differential Equations cites this paper.

Neuro-Symbolic AI for Analytical Solutions of Differential Equations Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-23T03:35:21.208401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4892433c-595d-4e46-9ba9-af70a315b0e5 · inbound

5D Neural Surrogates for Nonlinear Gyrokinetic Simulations of Plasma Turbulence cites this paper.

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

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-08T12:41:56.037974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c0dcf85-b53a-4281-b01c-88b89b9f8556 · inbound

BSA: Ball Sparse Attention for Large-scale Geometries cites this paper.

BSA: Ball Sparse Attention for Large-scale Geometries Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T00:52:50.537250Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2fd2d1f1-633b-40be-84bb-34d9bdbd3ac2 · inbound

Accurate and scalable exchange-correlation with deep learning cites this paper.

Accurate and scalable exchange-correlation with deep learning Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators

Reference 120

Resolution: verified exact; arxiv_id, observed 2026-05-19T09:07:13.964447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations cites this paper.

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

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T11:03:14.640288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96abec32-f943-49ac-8ea8-469dea6b55c0 · inbound

Di-BiLPS: Denoising induced Bidirectional Latent-PDE-Solver under Sparse Observations cites this paper.

Di-BiLPS: Denoising induced Bidirectional Latent-PDE-Solver under Sparse Observations Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-14T19:07:51.056263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b9ad82a9-1c69-489e-8b31-0275c2b5f226 · inbound

Data-Efficient Neural Operator Training via Physics-Based Active Learning cites this paper.

Data-Efficient Neural Operator Training via Physics-Based Active Learning Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T05:33:58.523662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c081aac7-deda-46a2-8620-6f2f36cc7ab0 · inbound

LEIA: Learned Environment for Interactive Architected Materials cites this paper.

LEIA: Learned Environment for Interactive Architected Materials Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-06-29T13:43:29.095635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 69a11f75-afb2-404e-a866-21838ef329dd · inbound

Hybrid Lagrangian-Eulerian Model for Lagrangian Fluid Simulation cites this paper.

Hybrid Lagrangian-Eulerian Model for Lagrangian Fluid Simulation Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators

Reference 131

Resolution: unresolved; no resolver link, observed 2026-08-06T00:30:47.777492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a38d7d9-c65b-46ad-8fc0-88ab44936d68 · inbound

tFUSOperator: Operator Learning for Transcranial Focused Ultrasound Digital Twins cites this paper.

tFUSOperator: Operator Learning for Transcranial Focused Ultrasound Digital Twins Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T19:54:14.937144Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.