Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Neural reparameterization improves structural optimization
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:1909.04240.
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 2026-08-15T15:55:52.485473Z
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 ea64f3a3-a594-4ba4-a20c-023d2323010b · inbound
Meta-neural Topology Optimization: Knowledge Infusion with Meta-learning cites this paper.
Meta-neural Topology Optimization: Knowledge Infusion with Meta-learning Neural reparameterization improves structural optimization
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T14:22:39.854384Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cc18b09e-68b4-47aa-bd2d-206fff64f18f · inbound
A Memory Efficient Adjoint Method to Enable Billion Parameter Optimization on a Single GPU in Dynamic Problems cites this paper.
A Memory Efficient Adjoint Method to Enable Billion Parameter Optimization on a Single GPU in Dynamic Problems Neural reparameterization improves structural optimization
Reference 97
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T15:55:52.485473Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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 2026-08-20T06:33:59.587034+00:00.
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 2026-08-20T06:33:59.587034+00:00.
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 2026-08-20T06:33:59.587034+00:00.