Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Towards General Neural Surrogate Solvers with Specialized Neural Accelerators
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2405.02351.
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 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T12:05:33.986477Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-16T18:28:14.710132Z
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 7ce97dd2-080d-4297-8d70-4fdb981163fb · inbound
Inferring Structure via Duality for Photonic Inverse Design cites this paper.
Inferring Structure via Duality for Photonic Inverse Design Towards General Neural Surrogate Solvers with Specialized Neural Accelerators
Reference 69
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T12:05:33.986477Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ced8bad7-edb7-47b6-8d02-fd482b192d1d · inbound
An Energy Stable Approach for Learning Derivative Operators from Noisy Data for Maxwells Equations cites this paper.
An Energy Stable Approach for Learning Derivative Operators from Noisy Data for Maxwells Equations Towards General Neural Surrogate Solvers with Specialized Neural Accelerators
Reference 18
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T18:28:14.712469Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation be2fa71d-1823-4b66-a180-789cc30ac8bc · inbound
An Energy Stable Approach for Learning Derivative Operators from Noisy Data for Maxwells Equations cites this paper.
An Energy Stable Approach for Learning Derivative Operators from Noisy Data for Maxwells Equations Towards General Neural Surrogate Solvers with Specialized Neural Accelerators
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T12:45:16.022303Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.