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Paper Citation Record · LEDGER

Accelerating multimodal gravitational waveforms from precessing compact binaries with artificial neural networks

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2205.14066.

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

pith.paper-citation-record.v1
2205.14066 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:05:21.952151Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T11:46:31.799342Z

Reference resolution

0 of 0 outbound references displayed

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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 7fb3450a-83ab-4267-9ef0-e1856dbc5322 · inbound

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

Surrogate modeling of gravitational waves microlensed by spherically symmetric potentials Accelerating multimodal gravitational waveforms from precessing compact binaries with artificial neural networks

Reference 90

Resolution
unresolved
no resolver link, observed 2026-08-10T22:05:21.952151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:05:21.952151Z digest=sha256:d020939506fad9742a53795054bf34945597723f529aad519b78b7c5040cc1d8

Observation 19189170-feb9-4c1c-ab39-0af7cd1e93f4 · 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 Accelerating multimodal gravitational waveforms from precessing compact binaries with artificial neural networks

Reference 81

Resolution
unresolved
no resolver link, observed 2026-08-10T13:11:53.382768Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T13:11:53.382768Z digest=sha256:8ef09c7a3971e1b0c59c391bf7fbaf1cb924fa4cd384fe58f63756007ea067b7

Observation ad07fd22-a95c-480a-a035-64f7cf6348d3 · 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 Accelerating multimodal gravitational waveforms from precessing compact binaries with artificial neural networks

Reference 91

Resolution
unresolved
no resolver link, observed 2026-08-04T13:34:57.542705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T13:34:57.542705Z digest=sha256:b72989adcb466d6985ab2a8993b9b6cf1a70487dfc2b6dba3e70acf4fa86c324

Observation 10621d1b-b583-4ccb-b693-247b5ddc39be · 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 Accelerating multimodal gravitational waveforms from precessing compact binaries with artificial neural networks

Reference 45

Resolution
verified exact
arxiv_id, observed 2026-05-11T11:46:31.820755Z

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.

source=pdf_text observed=2026-05-10T12:36:17.507439Z digest=sha256:97f096a46800dd654c71378e4076bc679a120f30c10cca0c73bd221a4a6f21bc

Observation 16004d35-e1a4-4e0d-8080-a661afb7a785 · 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 Accelerating multimodal gravitational waveforms from precessing compact binaries with artificial neural networks

Reference 57

Resolution
unresolved
no resolver link, observed 2026-07-31T04:58:16.823242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T04:58:16.823242Z digest=sha256:e9d93ac8bf4630990c7ecf70b226800ef00f42875643383dbb8cb6886f8b9e1f