Paper Citation Record · LEDGER

PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2402.00326.

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

pith.paper-citation-record.v1
2402.00326 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T17:45:05.805749Z

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
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malformed identifier0
metadata mismatch0

External citation measurements

23
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 86474204-12c8-48e2-9125-bbfab0fe76e6 · inbound

Deep Learning Alternatives of the Kolmogorov Superposition Theorem cites this paper.

Deep Learning Alternatives of the Kolmogorov Superposition Theorem PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-23T19:58:23.525197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 11af5d6b-09ec-40a3-bbc3-ca641ff24f94 · inbound

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks cites this paper.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-09T17:45:05.805749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2c7e2dc-73c8-44ca-8e82-8b7df54429b7 · inbound

Physics-informed neural networks for solving moving interface flow problems using the level set approach cites this paper.

Physics-informed neural networks for solving moving interface flow problems using the level set approach PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-09T12:13:42.697090Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce3e03ee-ffbe-4240-8054-d5d17d1a61e7 · inbound

Numerical Differentiation-based Electrophysiology-Aware Adaptive ResNet for Inverse ECG Modeling cites this paper.

Numerical Differentiation-based Electrophysiology-Aware Adaptive ResNet for Inverse ECG Modeling PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-23T03:25:20.668358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fcaca961-0a17-484f-a79c-718691a612c0 · inbound

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement cites this paper.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T14:41:36.081705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9831988e-965f-4c0f-99dc-77d0be6fe6d8 · inbound

ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms cites this paper.

ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-17T01:58:51.590401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 11705fce-ad2f-4152-8cfb-2596e1871686 · inbound

ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms cites this paper.

ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-03T18:51:34.232505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e87926e-3f41-48cc-b10b-b32467104e30 · inbound

Physics informed operator learning of parameter dependent spectra cites this paper.

Physics informed operator learning of parameter dependent spectra PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:26:14.548301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 808dde46-1c93-4dae-b935-f4d3fed7e4a1 · inbound

Uncovering Turbulent Dynamics in Stenotic Flows from 4D-flow MRI Measurements via Resolvent Analysis and Data Assimilation cites this paper.

Uncovering Turbulent Dynamics in Stenotic Flows from 4D-flow MRI Measurements via Resolvent Analysis and Data Assimilation PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks

Reference 127

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T08:01:46.042576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a596ad40-c3ed-439b-bcc0-c9cfba11cc75 · inbound

Trainable Spline Representations for Physics-Informed Learning cites this paper.

Trainable Spline Representations for Physics-Informed Learning PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T22:28:09.956404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.