Paper Citation Record · LEDGER

ODEFormer: Symbolic Regression of Dynamical Systems with Transformers

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2310.05573.

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

pith.paper-citation-record.v1
2310.05573 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 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 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T12:54:20.734400Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T13:48:21.216818Z

Reference resolution

0 of 0 outbound references displayed

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verified fuzzy0
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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 902fac89-b9da-490f-9635-da971a5ee75e · inbound

Scalable Mechanistic Neural Networks for Differential Equations and Machine Learning cites this paper.

Scalable Mechanistic Neural Networks for Differential Equations and Machine Learning ODEFormer: Symbolic Regression of Dynamical Systems with Transformers

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-23T19:33:22.120695Z

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 8ef536ea-2501-4cbf-83b0-964f4aaa4e7b · inbound

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations cites this paper.

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations ODEFormer: Symbolic Regression of Dynamical Systems with Transformers

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-08T12:54:20.734400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b30e216-f4fc-46a1-a6b1-ffc6ea7f6aad · inbound

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches cites this paper.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches ODEFormer: Symbolic Regression of Dynamical Systems with Transformers

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T19:20:41.304250Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d732cd1-5db9-486d-b26d-93ea1334b8ba · inbound

Neuro-Symbolic ODE Discovery with Latent Grammar Flow cites this paper.

Neuro-Symbolic ODE Discovery with Latent Grammar Flow ODEFormer: Symbolic Regression of Dynamical Systems with Transformers

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-10T08:48:02.527311Z

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 e2aab3b7-c895-4c21-81e9-7a456068cb62 · inbound

Neuro-Symbolic ODE Discovery with Latent Grammar Flow cites this paper.

Neuro-Symbolic ODE Discovery with Latent Grammar Flow ODEFormer: Symbolic Regression of Dynamical Systems with Transformers

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-15T12:27:15.229823Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6690155b-4ef5-45df-8c28-6eea0b82375a · inbound

FLUID: Continuous-Time Hyperconnected Sparse Transformer for Sink-Free Learning cites this paper.

FLUID: Continuous-Time Hyperconnected Sparse Transformer for Sink-Free Learning ODEFormer: Symbolic Regression of Dynamical Systems with Transformers

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:21:09.949656Z

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 72db36ab-1574-4628-936d-04e9d171b2eb · inbound

Discovery of Nonlinear Dynamics with Automated Basis Function Generation cites this paper.

Discovery of Nonlinear Dynamics with Automated Basis Function Generation ODEFormer: Symbolic Regression of Dynamical Systems with Transformers

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:21:25.750082Z

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 625d91fe-f061-460e-ba0d-c0492b74e97f · inbound

Extracting Governing Equations from Latent Dynamics via Multi-View Contrastive Learning cites this paper.

Extracting Governing Equations from Latent Dynamics via Multi-View Contrastive Learning ODEFormer: Symbolic Regression of Dynamical Systems with Transformers

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-07-03T13:48:21.218519Z

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 73f67822-03ef-474d-ab93-02cbf0e7002d · inbound

Neural operator discovery from heterogeneous trajectories cites this paper.

Neural operator discovery from heterogeneous trajectories ODEFormer: Symbolic Regression of Dynamical Systems with Transformers

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-31T23:47:25.979027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.