Paper Citation Record · LEDGER

Characterizing possible failure modes in physics-informed neural networks

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 17 inbound Pith citation observations for arXiv:2109.01050.

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

pith.paper-citation-record.v1
2109.01050 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 17 of 17 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 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:46:56.517375Z

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

115
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 5730da00-7834-4ddf-a4bc-75982e494887 · inbound

Operator-based machine learning framework for generalizable prediction of unsteady treatment dynamics in stormwater infrastructure cites this paper.

Operator-based machine learning framework for generalizable prediction of unsteady treatment dynamics in stormwater infrastructure Characterizing possible failure modes in physics-informed neural networks

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T19:46:56.517375Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5944cf6e-c4db-49a3-abf4-8245ca18a0ad · inbound

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches cites this paper.

Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches Characterizing possible failure modes in physics-informed neural networks

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T19:20:42.751130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 894fb16a-17cf-4e8f-8e7c-c4c5e872355d · inbound

Multi-Head Neural Operator for Modelling Interfacial Dynamics cites this paper.

Multi-Head Neural Operator for Modelling Interfacial Dynamics Characterizing possible failure modes in physics-informed neural networks

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-06T19:04:46.277430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af2510ae-92fc-415e-9f2b-da50efe072eb · inbound

Breaking the Precision Ceiling in Physics-Informed Neural Networks: A Hybrid Fourier-Neural Architecture for Ultra-High Accuracy cites this paper.

Breaking the Precision Ceiling in Physics-Informed Neural Networks: A Hybrid Fourier-Neural Architecture for Ultra-High Accuracy Characterizing possible failure modes in physics-informed neural networks

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T13:13:32.191508Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a753afe4-245e-4480-a3c8-9527f38efb83 · inbound

Towards Digital Twins for Optimal Radioembolization cites this paper.

Towards Digital Twins for Optimal Radioembolization Characterizing possible failure modes in physics-informed neural networks

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-05T13:48:47.603289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 840fd397-46ca-4d0d-b938-b5cef226346e · inbound

LieSolver: PDE-Constrained Learning for IBVPs via Lie Symmetries cites this paper.

LieSolver: PDE-Constrained Learning for IBVPs via Lie Symmetries Characterizing possible failure modes in physics-informed neural networks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T07:32:30.476380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df85f9b4-b3ce-4a78-ada2-4de741d9652e · inbound

Diagnosing Failure Modes of Neural Operators Across Diverse PDE Families cites this paper.

Diagnosing Failure Modes of Neural Operators Across Diverse PDE Families Characterizing possible failure modes in physics-informed neural networks

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-03T10:03:08.121324Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4be735f-3d12-42ac-a5b5-3ee95f9bbb0c · inbound

Cell-induced densification and tether formation in fibrous extracellular matrices with biomimetic physics-informed neural networks cites this paper.

Cell-induced densification and tether formation in fibrous extracellular matrices with biomimetic physics-informed neural networks Characterizing possible failure modes in physics-informed neural networks

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-14T00:33:30.433025Z

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 74ecd3b2-f158-4e00-af20-92842006d324 · inbound

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models cites this paper.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Characterizing possible failure modes in physics-informed neural networks

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-13T21:38:18.403879Z

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 7ba2277a-8801-4341-914c-4e697008f803 · inbound

When PINNs Go Wrong: Pseudo-Time Stepping Against Spurious Solutions cites this paper.

When PINNs Go Wrong: Pseudo-Time Stepping Against Spurious Solutions Characterizing possible failure modes in physics-informed neural networks

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:06:14.599714Z

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 7ad6cf16-cc69-4406-9af5-a6e0ea011b17 · inbound

Adaptive anisotropic composite quadratures for residual minimisation in neural PDE approximations cites this paper.

Adaptive anisotropic composite quadratures for residual minimisation in neural PDE approximations Characterizing possible failure modes in physics-informed neural networks

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-05-09T19:35:38.848801Z

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 bbd89a22-b76b-443d-84a8-fec743f6fd91 · inbound

Scale-Aware Adversarial Analysis: A Diagnostic for Generative AI in Multiscale Complex Systems cites this paper.

Scale-Aware Adversarial Analysis: A Diagnostic for Generative AI in Multiscale Complex Systems Characterizing possible failure modes in physics-informed neural networks

Reference 76

Resolution: metadata mismatch; arxiv_id, observed 2026-05-09T19:56:16.589665Z

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 6c58517e-472f-4f97-9844-07df016e0399 · inbound

Neural Spectral Element Methods for stiff multiphysics PDEs with electrochemical transport benchmarks cites this paper.

Neural Spectral Element Methods for stiff multiphysics PDEs with electrochemical transport benchmarks Characterizing possible failure modes in physics-informed neural networks

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-07-02T00:16:23.982870Z

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 0212c063-6c27-4418-945a-7565b7486b93 · inbound

A Convex Quasilinearization Method for Solving Nonlinear PDEs with Physics-Informed Neural Networks cites this paper.

A Convex Quasilinearization Method for Solving Nonlinear PDEs with Physics-Informed Neural Networks Characterizing possible failure modes in physics-informed neural networks

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-07-03T22:08:59.313379Z

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 e8b5aa18-5302-4c95-bc44-cb7c1887c779 · inbound

Physics-guided Convolutional Neural Network for Domain Growth Prediction in Systems with Conserved Kinetics cites this paper.

Physics-guided Convolutional Neural Network for Domain Growth Prediction in Systems with Conserved Kinetics Characterizing possible failure modes in physics-informed neural networks

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-07-03T04:37:37.250698Z

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 912def68-4edf-462d-901c-46bfc13b421e · inbound

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks cites this paper.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Characterizing possible failure modes in physics-informed neural networks

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T02:48:48.786974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6dbe8f9c-c3f8-4448-98a9-6f3453d45446 · inbound

Evolution-Level Quantum Optimal Control of Single-Qubit Gates with Physics-Informed Neural Networks cites this paper.

Evolution-Level Quantum Optimal Control of Single-Qubit Gates with Physics-Informed Neural Networks Characterizing possible failure modes in physics-informed neural networks

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-02T00:52:31.166818Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.