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Paper Citation Record · LEDGER

Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order models

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2409.05709.

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

pith.paper-citation-record.v1
2409.05709 v1

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 2026-08-15T06:32:42.880941+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T16:37:37.416596Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 9a3e4a07-9c96-4c4f-83de-bc43b2ad9ae2 · inbound

Latent feedback control of distributed systems in multiple scenarios through deep learning-based reduced order models cites this paper.

Latent feedback control of distributed systems in multiple scenarios through deep learning-based reduced order models Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order models

Reference 72

Resolution
unresolved
no resolver link, observed 2026-08-11T16:37:37.416596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T16:37:37.416596Z digest=sha256:5b8f03903998d00c66e720ad96ac2ccc2d45bdd03bc32b3b2c71dd85b4722976

Observation 99a2a926-3384-4462-a387-46bfb3a237a4 · inbound

Employing Deep Neural Operators for PDE control by decoupling training and optimization cites this paper.

Employing Deep Neural Operators for PDE control by decoupling training and optimization Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order models

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-05-19T11:47:16.010585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-19T11:43:59.085277Z digest=sha256:b88aadaaca6f47bdc95e95cb69e1c67531de5c7337e0d8ec283917c2803ce1ff

Observation 3dabd54f-5f9e-421f-b3a1-8718dd3d3293 · inbound

Local Linearity of LLMs Enables Activation Steering via Model-Based Linear Optimal Control cites this paper.

Local Linearity of LLMs Enables Activation Steering via Model-Based Linear Optimal Control Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order models

Reference 22

Resolution
verified exact
arxiv_id, observed 2026-05-10T02:32:49.475810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-05-10T02:31:07.932802Z digest=sha256:6e6fd61623a6d3f674ee0a0b87daa7174aae926f7fbc4e687097ae81306fab80