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Paper Citation Record · LEDGER

DyNODE: Neural Ordinary Differential Equations for Dynamics Modeling in Continuous Control

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2009.04278.

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

pith.paper-citation-record.v1
2009.04278 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T04:22:03.611646Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T20:30:07.269444Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

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 8d451a7f-4978-427c-9fe4-3e435e4effdb · inbound

Parallel-in-Time Training of Recurrent Neural Networks for Dynamical Systems Reconstruction cites this paper.

Parallel-in-Time Training of Recurrent Neural Networks for Dynamical Systems Reconstruction DyNODE: Neural Ordinary Differential Equations for Dynamics Modeling in Continuous Control

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-14T21:12:58.645145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-14T21:12:25.130529Z digest=sha256:e82ff527e125c30cfc1b28e358acf59ad6d50b8be7f529acad11380a6edf09f0

Observation d7967e7a-1140-49bc-837a-4403d0d48ced · inbound

Topological Out-of-Domain Generalization in Dynamical Systems Reconstruction cites this paper.

Topological Out-of-Domain Generalization in Dynamical Systems Reconstruction DyNODE: Neural Ordinary Differential Equations for Dynamics Modeling in Continuous Control

Reference 2

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T10:19:47.883082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-06-26T08:55:24.311225Z digest=sha256:40bff04f1770dcb9a36c44ed0bea1e7cec42ae21c99d11bb1245b7325cf0577f

Observation fd7adcd2-11f1-4a38-8c8a-a765368cfffc · inbound

$\text{DT}^2$: Decision-Targeted Digital Twins cites this paper.

$\text{DT}^2$: Decision-Targeted Digital Twins DyNODE: Neural Ordinary Differential Equations for Dynamics Modeling in Continuous Control

Reference 50

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T20:30:07.271237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-06-25T20:08:13.039445Z digest=sha256:165547084f3c1b028a9957f25653647f743188760b3a21397a575550a7ffb4e9

Observation 225bbfae-3de5-406f-b366-9b53dcd0df85 · inbound

A Minimal Interpretable Architecture for Zero-Shot Reconstruction of Dynamical Systems cites this paper.

A Minimal Interpretable Architecture for Zero-Shot Reconstruction of Dynamical Systems DyNODE: Neural Ordinary Differential Equations for Dynamics Modeling in Continuous Control

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-02T00:46:14.897310Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T00:46:14.897310Z digest=sha256:76c09b6aac847b18256a621b0fe912d76191c0d0ac63d0156d102505a089177d

Observation 6870f112-0947-4f9a-b755-fd78dfc22994 · inbound

Flowing Through States: Neural ODE Regularization for Reinforcement Learning cites this paper.

Flowing Through States: Neural ODE Regularization for Reinforcement Learning DyNODE: Neural Ordinary Differential Equations for Dynamics Modeling in Continuous Control

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-10T04:22:03.611646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T04:22:03.611646Z digest=sha256:41dde667fe37588a15ff0c43e5b049cf632f4818e993bd6e00b69a01c8c726ee