Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Deterministic and statistical calibration of constitutive models from full-field data with parametric physics-informed neural networks
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2405.18311.
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 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T22:13:09.417475Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-09T14:24:43.432561Z
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 3d3c8cc0-d196-4ac1-ae48-d0775d09b3f1 · inbound
Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion cites this paper.
Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion Deterministic and statistical calibration of constitutive models from full-field data with parametric physics-informed neural networks
Reference 2
- Resolution
- verified exact
- local_arxiv, observed 2026-08-09T14:24:43.437588Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation f7fc49ca-3ee1-495b-9196-e91828945883 · inbound
Automatically Differentiable Model Updating (ADiMU): conventional, hybrid, and neural network material model discovery including history-dependency cites this paper.
Automatically Differentiable Model Updating (ADiMU): conventional, hybrid, and neural network material model discovery including history-dependency Deterministic and statistical calibration of constitutive models from full-field data with parametric physics-informed neural networks
Reference 66
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T22:13:09.417475Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.