Paper Citation Record · LEDGER

Poseidon: Efficient Foundation Models for PDEs

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 20 inbound Pith citation observations for arXiv:2405.19101.

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

pith.paper-citation-record.v1
2405.19101 v2

Coverage vector

measured 0 of 0 reference resolution

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Source: paper_references, paper_reference_links

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 20 of 20 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T19:48:11.369630Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

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External citation measurements

9
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 836b86fb-2581-4ca8-8be4-26a0c2fa063d · inbound

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

Neuro-Symbolic AI for Analytical Solutions of Differential Equations Poseidon: Efficient Foundation Models for PDEs

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-23T03:35:21.221718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ecf6d2b7-ce09-4cf2-821d-8049f5a39c96 · inbound

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts cites this paper.

Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts Poseidon: Efficient Foundation Models for PDEs

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-08T19:48:11.369630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d717b93a-742d-41ec-9b16-60c28f7f962a · inbound

A Multimodal PDE Foundation Model for Prediction and Scientific Text Descriptions cites this paper.

A Multimodal PDE Foundation Model for Prediction and Scientific Text Descriptions Poseidon: Efficient Foundation Models for PDEs

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-08T17:02:12.747940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c969990c-7dda-42aa-95dd-e582e2614c20 · inbound

Latent Mamba Operator for Partial Differential Equations cites this paper.

Latent Mamba Operator for Partial Differential Equations Poseidon: Efficient Foundation Models for PDEs

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-07T14:24:44.027049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1783d56-600b-4c1b-b01e-3cd85d5695b6 · inbound

Autoregressive regularized score-based diffusion models for multi-scenarios fluid flow prediction cites this paper.

Autoregressive regularized score-based diffusion models for multi-scenarios fluid flow prediction Poseidon: Efficient Foundation Models for PDEs

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T12:36:57.885621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8850a0d6-b88f-4c5d-b7f8-c7a08a45cc3a · inbound

A Two-Phase Deep Learning Framework for Adaptive Time-Stepping in High-Speed Flow Modeling cites this paper.

A Two-Phase Deep Learning Framework for Adaptive Time-Stepping in High-Speed Flow Modeling Poseidon: Efficient Foundation Models for PDEs

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-05-19T10:22:14.677365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ce8c540d-87d1-4eaf-9cbb-5e4a9cb97301 · inbound

Mondrian: Transformer Operators via Domain Decomposition cites this paper.

Mondrian: Transformer Operators via Domain Decomposition Poseidon: Efficient Foundation Models for PDEs

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T05:23:48.368039Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19a6076e-d49a-4609-a94a-f0dd6496d82f · inbound

Physics-informed, boundary-constrained Gaussian process regression for the reconstruction of fluid flow fields cites this paper.

Physics-informed, boundary-constrained Gaussian process regression for the reconstruction of fluid flow fields Poseidon: Efficient Foundation Models for PDEs

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T14:56:04.700717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 748f3454-88e5-459b-a206-59382796e349 · inbound

Probabilistic operator learning: generative modeling and uncertainty quantification for foundation models of differential equations cites this paper.

Probabilistic operator learning: generative modeling and uncertainty quantification for foundation models of differential equations Poseidon: Efficient Foundation Models for PDEs

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T05:33:52.866755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96f98a0f-f136-483f-9737-89ef1e49ca25 · inbound

SuperWing: a comprehensive transonic wing dataset for data-driven aerodynamic design cites this paper.

SuperWing: a comprehensive transonic wing dataset for data-driven aerodynamic design Poseidon: Efficient Foundation Models for PDEs

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-16T21:51:17.836974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c7798586-fa34-4508-aa46-8aceeaa4ad44 · inbound

OmniMol: Transferring Particle Physics Knowledge to Molecular Dynamics with Point-Edge Transformers cites this paper.

OmniMol: Transferring Particle Physics Knowledge to Molecular Dynamics with Point-Edge Transformers Poseidon: Efficient Foundation Models for PDEs

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-16T13:20:57.976674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d389f757-21d6-44f3-bd0f-38a68e41bef6 · inbound

Towards Scaling Law Analysis For Spatiotemporal Weather Data cites this paper.

Towards Scaling Law Analysis For Spatiotemporal Weather Data Poseidon: Efficient Foundation Models for PDEs

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:40:50.790759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 31b9db4e-88a1-43de-81e4-ccd2a640aca4 · inbound

A Hybridizable Neural Time Integrator for Stable Autoregressive Forecasting cites this paper.

A Hybridizable Neural Time Integrator for Stable Autoregressive Forecasting Poseidon: Efficient Foundation Models for PDEs

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-10T00:14:46.504106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 161198cd-a4dc-48a5-8d70-c23090c3d628 · inbound

Function graph transformers universally approximate operators between function spaces cites this paper.

Function graph transformers universally approximate operators between function spaces Poseidon: Efficient Foundation Models for PDEs

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-20T13:13:18.753462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 939c279d-93f4-412f-bd16-2f3299723326 · inbound

ARC-STAR: Auditable Post-Hoc Correction for PDE Foundation Models cites this paper.

ARC-STAR: Auditable Post-Hoc Correction for PDE Foundation Models Poseidon: Efficient Foundation Models for PDEs

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-22T07:14:42.350324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d39cd300-d69f-4824-a68e-5b8c4fec0d8e · inbound

ARC-STAR: Auditable Post-Hoc Correction for PDE Foundation Models cites this paper.

ARC-STAR: Auditable Post-Hoc Correction for PDE Foundation Models Poseidon: Efficient Foundation Models for PDEs

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-25T05:46:39.682410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 415faf4b-754a-4f03-a7f1-4502ab19a692 · inbound

ARC-STAR: Auditable Post-Hoc Correction for PDE Foundation Models cites this paper.

ARC-STAR: Auditable Post-Hoc Correction for PDE Foundation Models Poseidon: Efficient Foundation Models for PDEs

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-06-30T17:44:57.781903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a2918fba-3cd3-4f94-b1d9-3d5546c182b3 · inbound

WLNO: Wavelet-Laplace Neural Operator for Solving Partial Differential Equations cites this paper.

WLNO: Wavelet-Laplace Neural Operator for Solving Partial Differential Equations Poseidon: Efficient Foundation Models for PDEs

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-06-30T15:04:46.388064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation face7293-b70e-4e22-8561-e22a355368c8 · inbound

Sequential Physics-Constrained Neural Operator Forward Modeling for the $\textit{Norne}$ Reservoir System cites this paper.

Sequential Physics-Constrained Neural Operator Forward Modeling for the $\textit{Norne}$ Reservoir System Poseidon: Efficient Foundation Models for PDEs

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-06-29T14:43:30.820910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4e13c1a4-1b81-4040-98cc-98ab3abf3de9 · inbound

Sparse probes and murky physics: a case study of interpretability challenges in a foundation model for continuum dynamics cites this paper.

Sparse probes and murky physics: a case study of interpretability challenges in a foundation model for continuum dynamics Poseidon: Efficient Foundation Models for PDEs

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-06-27T11:00:50.762057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.