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Paper Citation Record · LEDGER

Multiphase flow prediction with deep neural networks

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1910.09657.

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

pith.paper-citation-record.v1
1910.09657 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 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:04:46.203830Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T20:27:22.074079Z

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 7cf036af-308a-41f5-a4ec-1fc6f9fd1d67 · inbound

Multi-Head Neural Operator for Modelling Interfacial Dynamics cites this paper.

Multi-Head Neural Operator for Modelling Interfacial Dynamics Multiphase flow prediction with deep neural networks

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-06T19:04:46.203830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:04:46.203830Z digest=sha256:fb31a1a473c5a20ec7f13a5befd00bd55d43cf115916a289ecfd6174ac2f529b

Observation 319d3799-7b6a-4f9b-846d-ef2ac6602cf5 · inbound

A Comparative Study of Deep Learning Models for Geological Carbon Sequestration cites this paper.

A Comparative Study of Deep Learning Models for Geological Carbon Sequestration Multiphase flow prediction with deep neural networks

Reference 32

Resolution
verified exact
arxiv_id, observed 2026-07-02T20:27:22.076033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-27T20:31:07.789506Z digest=sha256:5699c09e1325b84074b234599a1f8e8ff9f33d715315bb6a48254d37db67f906