Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Lie Point Symmetry Data Augmentation for Neural PDE Solvers
As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2202.07643.
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 2026-08-12T06:34:41.77262+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T13:31:12.066157Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-21T05:33:58.510046Z
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 07bfb8e0-209f-43e8-bffd-4cc3e510f656 · inbound
Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates cites this paper.
Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Lie Point Symmetry Data Augmentation for Neural PDE Solvers
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T13:31:12.066157Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 241db744-e3d1-4d08-8f02-8feda96ea1ed · inbound
MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation cites this paper.
MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation Lie Point Symmetry Data Augmentation for Neural PDE Solvers
Reference 2023
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T13:56:27.019493Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d21774fb-5786-42b0-ab05-73f9572a040d · inbound
Data-Efficient Neural Operator Training via Physics-Based Active Learning cites this paper.
Data-Efficient Neural Operator Training via Physics-Based Active Learning Lie Point Symmetry Data Augmentation for Neural PDE Solvers
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T05:33:58.511916Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.