Paper Citation Record · LEDGER

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

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 21 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

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Source: paper_references, paper_reference_links

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 21 of 21 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T21:49:41.703034Z

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

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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 ba637434-e286-4877-a106-d15c77dd52d6 · inbound

Machine learning for modelling unstructured grid data in computational physics: a review cites this paper.

Machine learning for modelling unstructured grid data in computational physics: a review PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks

Reference 291

Resolution: unresolved; no resolver link, observed 2026-08-07T21:49:41.703034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53c51dd3-5fd3-4605-b0f3-758f16778186 · inbound

Hierarchical-embedding autoencoder with a predictor (HEAP) as efficient architecture for learning long-term evolution of complex multi-scale physical systems cites this paper.

Hierarchical-embedding autoencoder with a predictor (HEAP) as efficient architecture for learning long-term evolution of complex multi-scale physical systems PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-07T14:27:44.171921Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f90cf8b-b2cf-44bf-8c28-70f1962c67a3 · inbound

SPINN: Advancing Cosmological Simulations of Fuzzy Dark Matter with Physics Informed Neural Networks cites this paper.

SPINN: Advancing Cosmological Simulations of Fuzzy Dark Matter with Physics Informed Neural Networks PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-07T11:16:00.431695Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 789327a9-3f9b-456b-b837-a8f3a870c49b · inbound

Learning Mappings in Mesh-based Simulations cites this paper.

Learning Mappings in Mesh-based Simulations PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T00:50:33.901607Z

Source-reported events for the cited work

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Observation c8cad124-138e-4246-a334-d9c8caf05e87 · inbound

Towards Digital Twins for Optimal Radioembolization cites this paper.

Towards Digital Twins for Optimal Radioembolization PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-05T13:48:46.899564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Resolution: 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 2026-08-07T06:34:17.273281+00:00.

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

Resolution: 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 2026-08-07T06:34:17.273281+00:00.

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

Resolution: 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 2026-08-07T06:34:17.273281+00:00.

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

Resolution: 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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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

Resolution: 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 2026-08-07T06:34:17.273281+00:00.

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

Resolution: 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 2026-08-07T06:34:17.273281+00:00.

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

Resolution: 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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

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

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Unavailable: canonical work link unavailable.

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

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

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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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.