Paper Citation Record · LEDGER

Optimizing Neural Network Surrogate Models: Application to Black Hole Merger Remnants

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2501.16462.

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

pith.paper-citation-record.v1
2501.16462 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:42:34.320155Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T20:36:11.963693Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
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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 2d46e669-c352-403e-ade0-fe77659ccd8d · inbound

Chase Orbits, not Time: A Scalable Paradigm for Long-Duration Eccentric Gravitational-Wave Surrogates cites this paper.

Chase Orbits, not Time: A Scalable Paradigm for Long-Duration Eccentric Gravitational-Wave Surrogates Optimizing Neural Network Surrogate Models: Application to Black Hole Merger Remnants

Reference 106

Resolution: unresolved; no resolver link, observed 2026-08-04T13:34:57.585358Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8de35bfc-70a3-49d6-85fe-4f91cf809b43 · inbound

A comprehensive look into the accuracy of SpEC binary black hole waveforms cites this paper.

A comprehensive look into the accuracy of SpEC binary black hole waveforms Optimizing Neural Network Surrogate Models: Application to Black Hole Merger Remnants

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-04T11:14:43.588704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f014fd75-ad74-4e69-bba7-8024eafaf222 · inbound

Learning Post-Newtonian Corrections from Numerical Relativity cites this paper.

Learning Post-Newtonian Corrections from Numerical Relativity Optimizing Neural Network Surrogate Models: Application to Black Hole Merger Remnants

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-17T22:35:25.372179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 23f3211d-08e8-4ce7-a38b-20f25f198dff · inbound

Fast neural network surrogate for multimodal effective-one-body gravitational waveforms from generically precessing compact binaries cites this paper.

Fast neural network surrogate for multimodal effective-one-body gravitational waveforms from generically precessing compact binaries Optimizing Neural Network Surrogate Models: Application to Black Hole Merger Remnants

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-05-11T11:46:32.226357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 00c2efd1-f71c-4285-bb24-0dcb675cffe4 · inbound

Gravitational Waves from hybrid defects as probe of Flavor symmetry breaking: Machine-Learning Approach cites this paper.

Gravitational Waves from hybrid defects as probe of Flavor symmetry breaking: Machine-Learning Approach Optimizing Neural Network Surrogate Models: Application to Black Hole Merger Remnants

Reference 136

Resolution: verified exact; arxiv_id, observed 2026-07-01T20:36:11.964962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a1aa7230-0a7d-48e9-8273-765db79be85b · inbound

Fast, accurate, and differentiable: a neural-network surrogate for NRSur7dq4 precessing binary black hole waveforms cites this paper.

Fast, accurate, and differentiable: a neural-network surrogate for NRSur7dq4 precessing binary black hole waveforms Optimizing Neural Network Surrogate Models: Application to Black Hole Merger Remnants

Reference 59

Resolution: unresolved; no resolver link, observed 2026-07-31T04:58:16.829914Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e340d8a3-e1cf-41b5-95c4-40eb29fd93f7 · inbound

Unified remnant models for aligned-spin, precessing, and eccentric binary black hole mergers cites this paper.

Unified remnant models for aligned-spin, precessing, and eccentric binary black hole mergers Optimizing Neural Network Surrogate Models: Application to Black Hole Merger Remnants

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T00:42:34.320155Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.