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Paper Citation Record · LEDGER

Neural reparameterization improves structural optimization

As of 23 July 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1909.04240.

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

pith.paper-citation-record.v1
1909.04240 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-07-20T06:30:07.809122+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-09T02:48:18.362359Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T02:55:53.569328Z

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 a10dedb2-3c58-4284-b46d-a5a196cc06e5 · inbound

Large Language Models as Optimization Controllers: Adaptive Continuation for SIMP Topology Optimization cites this paper.

Large Language Models as Optimization Controllers: Adaptive Continuation for SIMP Topology Optimization Neural reparameterization improves structural optimization

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-05-19T17:37:41.768368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

source=pdf_text observed=2026-05-19T17:33:42.366794Z digest=sha256:2383ccd044fa4a476504ab75f1463a3a7a50defdba9fe20548bbc4282f98b0a7

Observation d875da47-d8c2-4acf-b695-7df4d01a4729 · inbound

Constraint-driven Optimization and Parametrization of Industrial NURBS Geometries via Neural Deformation Field cites this paper.

Constraint-driven Optimization and Parametrization of Industrial NURBS Geometries via Neural Deformation Field Neural reparameterization improves structural optimization

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-07-02T19:57:20.116280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

source=pdf_text observed=2026-06-27T21:09:46.177557Z digest=sha256:91e06e1b7a44c305abb88ee39ff507fc8d6cfdcb7db1e178149e1b2aa1d75949

Observation 0c6d413f-336e-4c29-9a72-e2900c700ba9 · inbound

Neural Operator-enabled Topology-informed Evolutionary Strategy for PDE-Constrained Optimization cites this paper.

Neural Operator-enabled Topology-informed Evolutionary Strategy for PDE-Constrained Optimization Neural reparameterization improves structural optimization

Reference 57

Resolution
metadata mismatch
local_arxiv, observed 2026-07-09T02:55:53.570664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

source=arxiv_source observed=2026-07-09T02:48:18.362359Z digest=sha256:28c5a75d851fb09b962099ec8adc0d8d0f0876c7e607bc6efe2bf1ad36312c3b