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Paper Citation Record · LEDGER

Neural empirical interpolation method for nonlinear model reduction

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2406.03562.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2406.03562 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:45:55.716628Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-12T21:30:15.584754Z

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 39a49824-d0f9-4b15-821c-499cf977ea00 · inbound

Optimal Transport-Based Displacement Interpolation with Data Augmentation for Reduced Order Modeling of Nonlinear Dynamical Systems cites this paper.

Optimal Transport-Based Displacement Interpolation with Data Augmentation for Reduced Order Modeling of Nonlinear Dynamical Systems Neural empirical interpolation method for nonlinear model reduction

Reference 46

Resolution
verified exact
local_arxiv, observed 2026-08-12T21:30:15.639927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-12T21:30:10.200195Z digest=sha256:ba8c7392971f9f19d8d6aff80c0996070c7cdacb9a523c5a542f86be1168785a

Observation 713336ed-70f6-47e1-bb24-b1a10d869a94 · inbound

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs cites this paper.

A hyperreduced manifold learning approach to nonlinear model order reduction for the homogenisation of hyperelastic RVEs Neural empirical interpolation method for nonlinear model reduction

Reference 112

Resolution
unresolved
no resolver link, observed 2026-08-15T16:45:55.716628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:45:55.716628Z digest=sha256:036b3e24700badaba6eab3e238893bcdd5a15b5df8bf0d8564c4bd7386cfd062