Paper Citation Record · LEDGER

PDEformer: Towards a Foundation Model for One-Dimensional Partial Differential Equations

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2402.12652.

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

pith.paper-citation-record.v1
2402.12652 v3

Coverage vector

measured 0 of 0 reference resolution

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Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:34:01.606885Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T09:29:43.186082Z

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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 a5e58f2b-0dd3-45d9-9ff8-64673718f0d7 · inbound

What You See is Not What You Get: Neural Partial Differential Equations and The Illusion of Learning cites this paper.

What You See is Not What You Get: Neural Partial Differential Equations and The Illusion of Learning PDEformer: Towards a Foundation Model for One-Dimensional Partial Differential Equations

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-12T14:34:01.606885Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5581f714-b980-43af-8dd9-6fb415c91079 · inbound

Point Cloud Neural Operator for Parametric PDEs on Complex and Variable Geometries cites this paper.

Point Cloud Neural Operator for Parametric PDEs on Complex and Variable Geometries PDEformer: Towards a Foundation Model for One-Dimensional Partial Differential Equations

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-10T15:12:46.302841Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46e87d8d-83f2-4bf1-a949-ee9345903824 · inbound

Domain Decomposition Subspace Neural Network Method for Solving Linear and Nonlinear Partial Differential Equations cites this paper.

Domain Decomposition Subspace Neural Network Method for Solving Linear and Nonlinear Partial Differential Equations PDEformer: Towards a Foundation Model for One-Dimensional Partial Differential Equations

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T13:52:38.108531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation caf6274c-8782-4a10-8b50-656c0d9b9a6a · inbound

Neural Interpretable PDEs: Harmonizing Fourier Insights with Attention for Scalable and Interpretable Physics Discovery cites this paper.

Neural Interpretable PDEs: Harmonizing Fourier Insights with Attention for Scalable and Interpretable Physics Discovery PDEformer: Towards a Foundation Model for One-Dimensional Partial Differential Equations

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T13:02:03.841967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 742c1372-ddd0-49bc-bb5b-0193ac50e9f6 · inbound

PolyMicros: Bootstrapping a Foundation Model for Polycrystalline Material Structure cites this paper.

PolyMicros: Bootstrapping a Foundation Model for Polycrystalline Material Structure PDEformer: Towards a Foundation Model for One-Dimensional Partial Differential Equations

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-07T14:56:23.251867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46f534cb-4d66-409d-9c6e-d1e61a16f874 · inbound

Flow marching for a generative PDE foundation model cites this paper.

Flow marching for a generative PDE foundation model PDEformer: Towards a Foundation Model for One-Dimensional Partial Differential Equations

Reference 63

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:51:25.567782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f4b3bff0-767b-4962-842f-8a37344a156a · inbound

A Mathematical Explanation of Transformers cites this paper.

A Mathematical Explanation of Transformers PDEformer: Towards a Foundation Model for One-Dimensional Partial Differential Equations

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-18T10:11:13.790596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b5a65db3-8408-4cb1-882d-c1774b300258 · inbound

GeoPT: Scaling Physics Simulation via Lifted Geometric Pre-Training cites this paper.

GeoPT: Scaling Physics Simulation via Lifted Geometric Pre-Training PDEformer: Towards a Foundation Model for One-Dimensional Partial Differential Equations

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-21T12:20:06.936481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c52904f3-a6d6-4bda-a182-84dd0aa2ab3d · inbound

MVNN: A Measure-Valued Neural Network for Learning McKean-Vlasov Dynamics from Particle Data cites this paper.

MVNN: A Measure-Valued Neural Network for Learning McKean-Vlasov Dynamics from Particle Data PDEformer: Towards a Foundation Model for One-Dimensional Partial Differential Equations

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-13T22:48:23.006724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6915e6d3-f996-477c-961c-3d7b89dee30b · inbound

IV-Net: A neural network for elliptic PDEs with random and highly varying coefficients cites this paper.

IV-Net: A neural network for elliptic PDEs with random and highly varying coefficients PDEformer: Towards a Foundation Model for One-Dimensional Partial Differential Equations

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-06-29T23:54:03.203617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4643d689-f3c4-4467-9ec0-5fae308b6bc6 · inbound

Harness In-Context Operator Learning with Chain of Operators cites this paper.

Harness In-Context Operator Learning with Chain of Operators PDEformer: Towards a Foundation Model for One-Dimensional Partial Differential Equations

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-07-03T08:17:46.071082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 11a4f704-3fe8-4544-a59e-6d853017e913 · inbound

PI-DOSnet: A Physics-Informed Deep Operator-Splitting Network for Evolution Partial Differential Equations cites this paper.

PI-DOSnet: A Physics-Informed Deep Operator-Splitting Network for Evolution Partial Differential Equations PDEformer: Towards a Foundation Model for One-Dimensional Partial Differential Equations

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-07-04T09:29:43.187931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.