Paper Citation Record · LEDGER

Gravitational-wave surrogate models powered by artificial neural networks: The ANN-Sur for waveform generation

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2008.12932.

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

pith.paper-citation-record.v1
2008.12932 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:42:50.609013Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T11:46:32.151008Z

Reference resolution

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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 05ef13c6-ba5f-4b91-9167-bb55ce38aa6a · inbound

Applications of machine learning in gravitational wave research with current interferometric detectors cites this paper.

Applications of machine learning in gravitational wave research with current interferometric detectors Gravitational-wave surrogate models powered by artificial neural networks: The ANN-Sur for waveform generation

Reference 238

Resolution: unresolved; no resolver link, observed 2026-08-11T11:42:50.609013Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ab11eec-e593-44d4-8c14-340704ce5281 · inbound

Surrogate modeling of gravitational waves microlensed by spherically symmetric potentials cites this paper.

Surrogate modeling of gravitational waves microlensed by spherically symmetric potentials Gravitational-wave surrogate models powered by artificial neural networks: The ANN-Sur for waveform generation

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-10T22:05:21.946078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6fe182b-0a27-49c9-b79a-4e6543b2ca1f · inbound

Optimizing Neural Network Surrogate Models: Application to Black Hole Merger Remnants cites this paper.

Optimizing Neural Network Surrogate Models: Application to Black Hole Merger Remnants Gravitational-wave surrogate models powered by artificial neural networks: The ANN-Sur for waveform generation

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-10T13:11:53.366245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3b32d6c-8d6d-46ab-9b53-20b61913d060 · 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 Gravitational-wave surrogate models powered by artificial neural networks: The ANN-Sur for waveform generation

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-04T13:34:57.534228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99128755-4dde-4593-8ac9-2073021eee3d · 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 Gravitational-wave surrogate models powered by artificial neural networks: The ANN-Sur for waveform generation

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-11T11:46:32.158089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8ea757d0-4db9-4f0c-a25f-6ac56cb05174 · 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 Gravitational-wave surrogate models powered by artificial neural networks: The ANN-Sur for waveform generation

Reference 56

Resolution: unresolved; no resolver link, observed 2026-07-31T04:58:16.819102Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.