Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Physics-Informed Neural Networks for Material Model Calibration from Full-Field Displacement Data
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2212.07723.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-04T05:38:20.779959Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-30T15:04:46.465534Z
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 ae3addd0-36a6-4ab3-9484-dd72c52ae4ad · inbound
Adaptive Material Fingerprinting for the fast discovery of polyconvex feature combinations in isotropic and anisotropic hyperelasticity cites this paper.
Adaptive Material Fingerprinting for the fast discovery of polyconvex feature combinations in isotropic and anisotropic hyperelasticity Physics-Informed Neural Networks for Material Model Calibration from Full-Field Displacement Data
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T00:20:52.184604Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 0cd065b7-fdcf-4969-bd5d-b8fd4e42869c · inbound
Adaptive Material Fingerprinting for the fast discovery of polyconvex feature combinations in isotropic and anisotropic hyperelasticity cites this paper.
Adaptive Material Fingerprinting for the fast discovery of polyconvex feature combinations in isotropic and anisotropic hyperelasticity Physics-Informed Neural Networks for Material Model Calibration from Full-Field Displacement Data
Reference 2022
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T05:38:20.779959Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 32aacb7a-c1d0-45dd-83bc-1bf787bbc94a · inbound
Finite Element-Based Material Learning via Automatic Differentiation: Learning constitutive neural network models from full-field deformation data cites this paper.
Finite Element-Based Material Learning via Automatic Differentiation: Learning constitutive neural network models from full-field deformation data Physics-Informed Neural Networks for Material Model Calibration from Full-Field Displacement Data
Reference 12
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T15:04:46.466941Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.