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Paper Citation Record · LEDGER

PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks

As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:2307.11833.

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

pith.paper-citation-record.v1
2307.11833 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T19:36:17.508809Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T18:30:02.715063Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation d2223b3e-6dd1-48d0-85b7-c5569fc65994 · inbound

Neural Multiscale Decomposition for Solving The Nonlinear Klein-Gordon Equation with Time Oscillation cites this paper.

Neural Multiscale Decomposition for Solving The Nonlinear Klein-Gordon Equation with Time Oscillation PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks

Reference 2022

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unresolved
no resolver link, observed 2026-08-03T19:36:17.508809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T19:36:17.508809Z digest=sha256:2e7f5022289c251ea1fe3cc46205572e3973d042e5d122d98277af111f002f6f

Observation ecdea05b-c48b-4f08-a647-0b6184923c4a · inbound

Integrating Fourier Neural Operator with Diffusion Model for Autoregressive Predictions of Three-dimensional Turbulence cites this paper.

Integrating Fourier Neural Operator with Diffusion Model for Autoregressive Predictions of Three-dimensional Turbulence PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks

Reference 82

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unresolved
no resolver link, observed 2026-08-03T16:37:40.545502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T16:37:40.545502Z digest=sha256:ef4ded73b408f886f2583c7cdfda780b3f3ce8e8d4e684b43cf90e646e6da9d0

Observation e7c69c31-27c2-485e-8ab0-bc4676d01c00 · inbound

A Simple but Efficient Transformer-Based Physics-Informed Neural Network for Incompressible Navier--Stokes Equations cites this paper.

A Simple but Efficient Transformer-Based Physics-Informed Neural Network for Incompressible Navier--Stokes Equations PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks

Reference 22

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verified exact
arxiv_id, observed 2026-05-21T17:05:24.294087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-05-21T17:04:21.164340Z digest=sha256:be8dbf164e0f51bd0eb51d23758908375918a0054057ebc07796092cb855ab36

Observation c6c4940b-df26-4ac1-83b9-3b06637ff493 · inbound

When PINNs Go Wrong: Pseudo-Time Stepping Against Spurious Solutions cites this paper.

When PINNs Go Wrong: Pseudo-Time Stepping Against Spurious Solutions PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks

Reference 30

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metadata mismatch
arxiv_id, observed 2026-05-11T21:06:14.680626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-05-08T06:43:07.836039Z digest=sha256:c15479f20b674fd383f58c138fa7d5462f2cd2cc542df0685fd6ba143f57663f

Observation 0a5f7b86-25a9-4b9b-92d2-04492db7692f · inbound

Can Transformers predict system collapse in dynamical systems? cites this paper.

Can Transformers predict system collapse in dynamical systems? PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks

Reference 51

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verified exact
arxiv_id, observed 2026-05-12T10:41:31.553857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-05-07T03:58:36.225134Z digest=sha256:25f106c9ab743a56e4d43897e6d75b7fdf3b3a79579af6745118c719efcbc4d5

Observation 4651642f-32cf-420b-b917-f130d60b854c · inbound

Deep Wave Network for Modeling Multi-Scale Physical Dynamics cites this paper.

Deep Wave Network for Modeling Multi-Scale Physical Dynamics PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks

Reference 27

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metadata mismatch
arxiv_id, observed 2026-05-11T17:31:04.852599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-05-08T17:33:24.661591Z digest=sha256:2cb7c9e9f19da21252474af35258ca9edcf494dd035d166ddc9b2cbf3d2b8421

Observation 13543649-cb1d-47b1-bdf9-0d1c88e92d39 · inbound

Physics-Informed Neural Networks with Attention Feature Expansion for Monge-Amp\`ere Equations cites this paper.

Physics-Informed Neural Networks with Attention Feature Expansion for Monge-Amp\`ere Equations PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks

Reference 50

Resolution
verified exact
arxiv_id, observed 2026-05-22T04:16:02.619108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-05-22T04:15:15.399040Z digest=sha256:262b9fa70971985bab92330331a4cf2724436be5393ebbc69b70d2abef535d4d

Observation 6a485b04-cb40-43b9-9b82-fbf4696c4da2 · inbound

Multi-Scale Separable Fourier Neural Networks for Solving High-Frequency PDEs cites this paper.

Multi-Scale Separable Fourier Neural Networks for Solving High-Frequency PDEs PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks

Reference 26

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metadata mismatch
arxiv_id, observed 2026-06-29T00:02:49.782873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-28T23:57:43.147283Z digest=sha256:36f435184b2c5eab4bb23d708829998f2fbd347bdcadcf8524aea7da0af513cb

Observation d2f10747-8bfc-47c7-a70b-c9e4c2c64c30 · inbound

Curvature-aware dynamic precision approach for physics-informed neural networks cites this paper.

Curvature-aware dynamic precision approach for physics-informed neural networks PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks

Reference 53

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metadata mismatch
arxiv_id, observed 2026-07-02T06:06:41.648172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-28T07:33:10.182691Z digest=sha256:3464fb7b9677e6c27e2afbb51b1fce6c3722efa3aba103ea91fc16ea5e957a2e

Observation 07ac87ac-9016-42bf-967d-60eeb586c8da · inbound

Physics-Informed Neural Network with Squeeze-Excitation-like Attention cites this paper.

Physics-Informed Neural Network with Squeeze-Excitation-like Attention PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks

Reference 39

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verified exact
arxiv_id, observed 2026-07-04T03:29:30.203869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-26T18:02:39.721547Z digest=sha256:c2c16642765599c003b8c9e14d06a5e50c1a57e5149e22e46ffdaa25118e7a21

Observation 66bac173-c9f1-4b88-8807-88b287ccfc88 · inbound

A Physics-Informed Fourier-Wavelet Transformer for Multiscale Computational Fluid Dynamics Surrogate Modeling cites this paper.

A Physics-Informed Fourier-Wavelet Transformer for Multiscale Computational Fluid Dynamics Surrogate Modeling PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-07-04T18:30:02.716937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-25T22:47:05.775339Z digest=sha256:35fdf326813adc9a2dca7b557339d46f5e200f59465e01a4f6ab04fb773c6178

Observation 8d3f8a16-7a84-4c26-a169-2e6a07d7a487 · inbound

LLT: Local Linear Transformer for PDE Operator Learning cites this paper.

LLT: Local Linear Transformer for PDE Operator Learning PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks

Reference 35

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unresolved
no resolver link, observed 2026-07-11T23:44:26.734234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T23:44:26.734234Z digest=sha256:e86ea40512130c10480a45ed0d8a9c00fdea625ae368a818e3fc452bfe535d0c

Observation c4980f34-7c04-43d2-825f-e33f7012b1fd · inbound

A new strategy for physics-informed neural networks based on hierarchical collocation point refinement cites this paper.

A new strategy for physics-informed neural networks based on hierarchical collocation point refinement PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks

Reference 6

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unresolved
no resolver link, observed 2026-08-02T01:32:23.088920Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T01:32:23.088920Z digest=sha256:360ce507fb7600e150eedd16dcaee6fc1e7cf5985fd1ceb47c8fa2771ea72bf1

Observation 6183cf8b-4dc3-4209-8b53-ee911760b0db · inbound

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology cites this paper.

Harnessing Machine Learning for Hybrid Constitutive Modelling of Viscoelastic Fluid Flows in Computational Rheology PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks

Reference 28

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unresolved
no resolver link, observed 2026-08-02T00:44:05.405259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T00:44:05.405259Z digest=sha256:b15df094eafae911581fb826cf951ce3fea09a4289a4c974b86d7bafa502e07a

Observation d1eca3f9-bc46-4c95-b7f4-54355f4b9135 · inbound

Split Complex-Valued Physics-Informed Neural Networks for Forward and Inverse Nonlinear PDEs cites this paper.

Split Complex-Valued Physics-Informed Neural Networks for Forward and Inverse Nonlinear PDEs PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks

Reference 59

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unresolved
no resolver link, observed 2026-08-02T00:14:59.506851Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T00:14:59.506851Z digest=sha256:d6826ded2bbd64d81c0f76261939ac63c9238eec8db6797a6747b2f3c919fc1a

Observation 531f38f6-04de-475c-bb3f-21c51d12b9ee · inbound

Cosmo-SPINN: Fuzzy Dark Matter Simulations with Physics-Informed Generative Networks cites this paper.

Cosmo-SPINN: Fuzzy Dark Matter Simulations with Physics-Informed Generative Networks PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks

Reference 64

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unresolved
no resolver link, observed 2026-07-31T02:27:16.981188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-31T02:27:16.981188Z digest=sha256:fc133b26ca4848c7ec31379842ee09a5ce71b506e83c109e73f39f1103de31aa