Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
A Robust SINDy Approach by Combining Neural Networks and an Integral Form
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2309.07193.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T15:27:43.222582Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T09:07:48.110474Z
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 a9509117-2154-449f-ac66-164b9d45affb · inbound
Sparse Discovery of Functional Relationships in Solutions to Systems of Differential Equations cites this paper.
Sparse Discovery of Functional Relationships in Solutions to Systems of Differential Equations A Robust SINDy Approach by Combining Neural Networks and an Integral Form
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:27:43.222582Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c7624545-10bf-44d1-88fa-39673df71fc0 · inbound
How Low Can You Go? Active Learning for Sparse Model Discovery in the Ultra-Low-Data Limit cites this paper.
How Low Can You Go? Active Learning for Sparse Model Discovery in the Ultra-Low-Data Limit A Robust SINDy Approach by Combining Neural Networks and an Integral Form
Reference 51
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T09:07:48.111883Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 659751ed-d81e-4777-b505-9372b20ecde8 · inbound
On the post-hoc Evaluation of PDE Discovery: A Multifaceted Challenge of Scientific Advancement cites this paper.
On the post-hoc Evaluation of PDE Discovery: A Multifaceted Challenge of Scientific Advancement A Robust SINDy Approach by Combining Neural Networks and an Integral Form
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-07-30T13:41:00.401372Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.