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Paper Citation Record · LEDGER

Neural Ordinary Differential Equations for Data-Driven Reduced Order Modeling of Environmental Hydrodynamics

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2104.13962.

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

pith.paper-citation-record.v1
2104.13962 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:34:09.065111Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T08:06:47.702996Z

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 2061a3a6-40b4-46d1-a847-a27d3bcab982 · inbound

Time Resolution Independent Operator Learning cites this paper.

Time Resolution Independent Operator Learning Neural Ordinary Differential Equations for Data-Driven Reduced Order Modeling of Environmental Hydrodynamics

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-06T20:34:09.065111Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:34:09.065111Z digest=sha256:ef02c2fed859a8b0a45c7dcf39d9c7f9f16052598fcc895f152f962fde7a3510

Observation f72ead0e-2e6c-485e-9ee9-d2f0d3dd3f18 · inbound

Convolutional Symmetric AutoEncoders: enhancing latent stability via differential geometry cites this paper.

Convolutional Symmetric AutoEncoders: enhancing latent stability via differential geometry Neural Ordinary Differential Equations for Data-Driven Reduced Order Modeling of Environmental Hydrodynamics

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-07-02T08:06:47.704323Z

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.

source=pdf_text observed=2026-07-02T08:02:44.126467Z digest=sha256:542ff1830af2425623940aefc90bde28ca2bb306c6b1ca58756f740376373f49