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Paper Citation Record · LEDGER

PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 18 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

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Source: paper_references, paper_reference_links

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 18 of 18 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:10:25.460458Z

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

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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

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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 2026-08-22T06:32:14.747728+00:00.

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Observation 074c113d-ecbd-4e65-8fd1-8cac76dc486d · inbound

PIG: Physics-Informed Gaussians as Adaptive Parametric Mesh Representations cites this paper.

PIG: Physics-Informed Gaussians as Adaptive Parametric Mesh Representations PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks

Reference 65

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Observation f49134fb-60cd-4115-ac6e-8ec2a52cb33e · inbound

KKANs: Kurkova-Kolmogorov-Arnold Networks and Their Learning Dynamics cites this paper.

KKANs: Kurkova-Kolmogorov-Arnold Networks and Their Learning Dynamics PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks

Reference 59

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no resolver link, observed 2026-08-11T10:23:14.194555Z

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Observation d8479803-0037-4742-8a4b-0e3f7d44bb20 · inbound

Are Two Hidden Layers Still Enough for the Physics-Informed Neural Networks? cites this paper.

Are Two Hidden Layers Still Enough for the Physics-Informed Neural Networks? PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks

Reference 22

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no resolver link, observed 2026-08-11T00:55:34.357038Z

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Observation 7cbb1ef5-fa1a-4d42-9549-5216ceac37d3 · inbound

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks cites this paper.

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks

Reference 19

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no resolver link, observed 2026-08-10T23:15:24.624240Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:15:24.624240Z digest=sha256:46f382ef735a69b3b9872783fb9a7ff875a7cbd6302b434b57406db874f882cb

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

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

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Unavailable: canonical work link unavailable.

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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

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

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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

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation ba88b6d7-7758-4cac-8410-7b33b29988bc · inbound

Physics-informed Temporal Difference Metric Learning for Robot Motion Planning cites this paper.

Physics-informed Temporal Difference Metric Learning for Robot Motion Planning PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks

Reference 50

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Observation 2925f43d-ad14-4fda-95c3-1e293195d103 · inbound

Mask-PINNs: Mitigating Internal Covariate Shift in Physics-Informed Neural Networks cites this paper.

Mask-PINNs: Mitigating Internal Covariate Shift in Physics-Informed Neural Networks PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks

Reference 15

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no resolver link, observed 2026-08-15T22:51:09.318124Z

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Observation 16899801-9c5f-4f09-9323-71f137d1e1e1 · inbound

Mollifier Layers: Enabling Efficient High-Order Derivatives in Inverse PDE Learning cites this paper.

Mollifier Layers: Enabling Efficient High-Order Derivatives in Inverse PDE Learning PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks

Reference 59

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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

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Observation b7083b71-9065-43cf-a68d-5e2f628fb41c · inbound

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design cites this paper.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks

Reference 73

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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 2026-08-22T06:32:14.747728+00:00.

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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

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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

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

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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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

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

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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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

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