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Paper Citation Record · LEDGER

Sub-Sequential Physics-Informed Learning with State Space Model

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2502.00318.

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

pith.paper-citation-record.v1
2502.00318 v2

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 2026-08-07T06:34:17.273281+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:16:00.005795Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T06:06:41.617423Z

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 b13efeb8-4952-4e41-94b4-5944266f9907 · inbound

SPINN: Advancing Cosmological Simulations of Fuzzy Dark Matter with Physics Informed Neural Networks cites this paper.

SPINN: Advancing Cosmological Simulations of Fuzzy Dark Matter with Physics Informed Neural Networks Sub-Sequential Physics-Informed Learning with State Space Model

Reference 85

Resolution
unresolved
no resolver link, observed 2026-08-07T11:16:00.005795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:16:00.005795Z digest=sha256:99047ccc75131f7ce36de17aee0a969943a643faa418033752f8e69728a5fc57

Observation 0db9d52d-9e08-4855-918d-372ea51621f8 · inbound

PIANO: Physics Informed Autoregressive Network cites this paper.

PIANO: Physics Informed Autoregressive Network Sub-Sequential Physics-Informed Learning with State Space Model

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-05T17:29:36.929006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T17:29:36.929006Z digest=sha256:4954be80af5f82d0cb89378698e7cb18b74eb975de0b5ad447dbd37547a716f8

Observation fb632feb-d47b-4d15-a5e3-a7be04ed6e60 · inbound

Neural Multiscale Decomposition for Solving The Nonlinear Klein-Gordon Equation with Time Oscillation cites this paper.

Neural Multiscale Decomposition for Solving The Nonlinear Klein-Gordon Equation with Time Oscillation Sub-Sequential Physics-Informed Learning with State Space Model

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-03T19:36:17.466777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T19:36:17.466777Z digest=sha256:cfd8c6016c71f6d345cb266e50c7f854f974c412936baa55b8bb10b6c2cc0237

Observation 38db5de9-6ef6-4258-8ab1-220ea3b11499 · inbound

Curvature-aware dynamic precision approach for physics-informed neural networks cites this paper.

Curvature-aware dynamic precision approach for physics-informed neural networks Sub-Sequential Physics-Informed Learning with State Space Model

Reference 49

Resolution
verified exact
arxiv_id, observed 2026-07-02T06:06:41.618886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-28T07:33:10.182691Z digest=sha256:8766e5f548ebfd9814ef4473d508186a70f985ac5f0fcf03a88242b76467f224

Observation 1e48c348-2337-4f53-9281-12e1b42ba027 · inbound

Cosmo-SPINN: Fuzzy Dark Matter Simulations with Physics-Informed Generative Networks cites this paper.

Cosmo-SPINN: Fuzzy Dark Matter Simulations with Physics-Informed Generative Networks Sub-Sequential Physics-Informed Learning with State Space Model

Reference 15

Resolution
unresolved
no resolver link, observed 2026-07-31T02:27:16.811259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-31T02:27:16.811259Z digest=sha256:ed216eaee867275f90e63a92770533d5d4a975f0ef437606746ed2d14f1286f8