Paper Citation Record · LEDGER

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth

As of 20 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2510.02872.

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pith.paper-citation-record.v1
2510.02872 v4

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measured 39 of 39 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-04T12:43:36.469540Z

measured 39 of 39 standing notices

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39 of 39 outbound references displayed

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Outbound references

Observation 9035e194-97d6-468a-9b79-d7b1648a0952 · outbound

This paper cites Iannuzzi and G.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Iannuzzi and G

Reference 1

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Observation 6c9cdd55-0428-4146-909d-94d23bedcca6 · outbound

This paper cites Sustainable corrosion inhibitors: A key step towards environmentally responsible corrosion control.Ain Shams Engineering Journal, 15(5):102672, 2024.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Sustainable corrosion inhibitors: A key step towards environmentally responsible corrosion control.Ain Shams Engineering Journal, 15(5):102672, 2024

Reference 2

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Observation 96ebcc41-85c0-4391-9c09-782056502c51 · outbound

This paper cites Singh and E.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Singh and E

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T12:43:31.788973Z

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Observation 21e1242a-d528-4333-9338-d7cf662814b9 · outbound

This paper cites Fu, Pakpoom Buabthong, Zachary Philip Ifkovits, Weilai Yu, Bruce S.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Fu, Pakpoom Buabthong, Zachary Philip Ifkovits, Weilai Yu, Bruce S

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T12:43:31.967122Z

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Observation d0392ca1-7ed3-49fc-b799-35fd0a2db976 · outbound

This paper cites Origin of nanoscale heterogeneity in the surface oxide film protecting stainless steel against corrosion.npj Materials Degradation, 3(1):29, 2019.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Origin of nanoscale heterogeneity in the surface oxide film protecting stainless steel against corrosion.npj Materials Degradation, 3(1):29, 2019

Reference 5

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Observation 397ea337-a8a3-465e-ab22-9ed3967bf766 · outbound

This paper cites Current developments of nanoscale insight into corrosion protection by passive oxide films.Current Opinion in Solid State and Materials Science, 22(4):156–167, 2018.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Current developments of nanoscale insight into corrosion protection by passive oxide films.Current Opinion in Solid State and Materials Science, 22(4):156–167, 2018

Reference 6

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Observation 4acf0f65-e512-4e75-bd19-4d3eed592487 · outbound

This paper cites Macdonald.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Macdonald

Reference 7

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Observation d2cb24c0-b076-49e9-85f0-60ad842c033e · outbound

This paper cites Oxide Film Growth Kinetics on Metals and Alloys: I.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Oxide Film Growth Kinetics on Metals and Alloys: I

Reference 8

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Observation 0af942c7-0f19-41eb-a1a0-7185816ec008 · outbound

This paper cites Oxide Film Growth Kinetics on Metals and Alloys: II.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Oxide Film Growth Kinetics on Metals and Alloys: II

Reference 9

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Observation 7958789f-cd03-4b36-9ce8-4259f35fcc81 · outbound

This paper cites Modeling electrochemical oxide film growth—passive and transpassive behavior of iron electrodes in halide-free solution.npj Materials Degradation, 7(1):53, June 2023.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Modeling electrochemical oxide film growth—passive and transpassive behavior of iron electrodes in halide-free solution.npj Materials Degradation, 7(1):53, June 2023

Reference 10

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Observation c0474f7d-456d-4ba4-a460-ce9539593df3 · outbound

This paper cites Modeling and simulation of passive film formation and breakdown in chloride ion containing electrolytes – a point defect model extension.Corrosion Science, 256:113166, 2025.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Modeling and simulation of passive film formation and breakdown in chloride ion containing electrolytes – a point defect model extension.Corrosion Science, 256:113166, 2025

Reference 11

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Observation 8f6146c5-5d3a-4075-acc3-ab2aec611a5c · outbound

This paper cites Macdonald, Jie Yang, Jie Qiu, and Shuzhong Wang.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Macdonald, Jie Yang, Jie Qiu, and Shuzhong Wang

Reference 12

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Observation e00466ab-427e-41a9-befd-d21bec87229f · outbound

This paper cites Kolotinskii, V .S.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Kolotinskii, V .S

Reference 13

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Observation 2cc9a5ef-bff4-4795-a01c-af1fb9f9e6a7 · outbound

This paper cites Modeling of a growing oxide film: The iron/iron oxide system.Journal of The Electrochemical Society, 142(5):1423–1430, 1995.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Modeling of a growing oxide film: The iron/iron oxide system.Journal of The Electrochemical Society, 142(5):1423–1430, 1995

Reference 14

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Observation 271e009f-8bc5-4881-9432-642a3f4ca0ff · outbound

This paper cites Engelhardt, Dihao Chen, Chaofang Dong, and Digby D.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Engelhardt, Dihao Chen, Chaofang Dong, and Digby D

Reference 15

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Observation d05c93b4-dd16-4edb-b71d-7b75f572af3d · outbound

This paper cites Bataillon, F.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Bataillon, F

Reference 16

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Observation 5309f142-9ff5-4f12-8371-6a67b5af2a4a · outbound

This paper cites Macdonald.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Macdonald

Reference 17

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Observation e10c3257-7c9d-4024-ac96-f97500a857ba · outbound

This paper cites Raissi, P.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Raissi, P

Reference 18

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Observation 912c3107-2924-4166-a3d1-eff0f38fbaed · outbound

This paper cites Physics-Informed Neural Networks for Electrical Circuit Analysis: Applications in Dielectric Material Modeling.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Physics-Informed Neural Networks for Electrical Circuit Analysis: Applications in Dielectric Material Modeling

Reference 19

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Observation 2fad9002-255b-491c-9133-f2e044a9b93d · outbound

This paper cites PF-PINNs: Physics-informed neural networks for solving coupled allen-cahn and cahn-hilliard phase field equations.Journal of Computational Physics, 529:113843, 2025.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth PF-PINNs: Physics-informed neural networks for solving coupled allen-cahn and cahn-hilliard phase field equations.Journal of Computational Physics, 529:113843, 2025

Reference 20

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Observation f4a80d68-5ff5-4cdc-9595-8df4875857ae · outbound

This paper cites Predicting voltammetry using physics-informed neural networks.The Journal of Physical Chemistry Letters, 13(2):536–543, 2022.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Predicting voltammetry using physics-informed neural networks.The Journal of Physical Chemistry Letters, 13(2):536–543, 2022

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T12:43:34.067336Z

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Observation b918cdcf-70f1-4aaf-8640-0085f9eda996 · outbound

This paper cites an unresolved cited work.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Unresolved cited work

Reference 22

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Observation 3efd5033-8996-4a42-b246-2cd763a81af1 · outbound

This paper cites A comprehensive analysis of PINNs: Variants, Applications, and Challenges.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth A comprehensive analysis of PINNs: Variants, Applications, and Challenges

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T12:43:34.382439Z

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Observation 0956d21d-1d1c-4a17-bc9c-ecaa027fdd74 · outbound

This paper cites A physics- informed neural network framework for multi-physics coupling microfluidic problems.Computers & Fluids, 284:106421, November 2024.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth A physics- informed neural network framework for multi-physics coupling microfluidic problems.Computers & Fluids, 284:106421, November 2024

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T12:43:34.482701Z

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Observation 05cebcaa-f8fb-4c6e-9d13-3ba964c3f4c5 · outbound

This paper cites A Physics Informed Neural Network (PINN) Methodology for Coupled Moving Boundary PDEs.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth A Physics Informed Neural Network (PINN) Methodology for Coupled Moving Boundary PDEs

Reference 25

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Observation 22f47ada-ff34-4d44-9775-a9034a134231 · outbound

This paper cites Is it time to swish? Comparing activation functions in solving the Helmholtz equation using physics-informed neural networks.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Is it time to swish? Comparing activation functions in solving the Helmholtz equation using physics-informed neural networks

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T12:43:34.726023Z

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Observation 4a0fe769-a2c0-49f5-b205-dc434bc59502 · outbound

This paper cites A comparative study of dimensional and non-dimensional inputs in physics-informed and data-driven neural networks for single-droplet evaporation.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth A comparative study of dimensional and non-dimensional inputs in physics-informed and data-driven neural networks for single-droplet evaporation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T12:43:34.832902Z

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Observation e56c2682-3414-43fd-8381-1e6a5cee7448 · outbound

This paper cites Neural Tangent Kernel: Convergence and Generalization in Neural Networks.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Neural Tangent Kernel: Convergence and Generalization in Neural Networks

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T12:43:34.976985Z

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Observation 075a9766-caef-42ec-aded-f9c724336840 · outbound

This paper cites PF-PINNs: Physics-informed neural networks for solving coupled Allen-Cahn and Cahn-Hilliard phase field equations.Journal of Computational Physics, 529:113843, May 2025.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth PF-PINNs: Physics-informed neural networks for solving coupled Allen-Cahn and Cahn-Hilliard phase field equations.Journal of Computational Physics, 529:113843, May 2025

Reference 29

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Observation 634b09ad-ee03-4c93-848d-3aaf7cc40c94 · outbound

This paper cites Enhanced Physics-Informed Neural Networks with Augmented Lagrangian Relaxation Method (AL-PINNs).

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Enhanced Physics-Informed Neural Networks with Augmented Lagrangian Relaxation Method (AL-PINNs)

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T12:43:35.216829Z

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Observation 1d679bc7-602a-4d91-b33e-6e89fbcd4355 · outbound

This paper cites PHYSICS-INFORMED NEURAL NETWORKS WITH CURRICULUM TRAINING FOR POROELASTIC FLOW AND DEFORMATION PROCESSES.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth PHYSICS-INFORMED NEURAL NETWORKS WITH CURRICULUM TRAINING FOR POROELASTIC FLOW AND DEFORMATION PROCESSES

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-04T12:43:35.322459Z

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Observation 9e4d212c-0167-404a-b7b2-167643bb7381 · outbound

This paper cites Visualizing the Loss Landscape of Neural Nets.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Visualizing the Loss Landscape of Neural Nets

Reference 32

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Observation 6f644cb6-3c07-4ebd-88ec-a3d16cf9f106 · outbound

This paper cites Deep Residual Learning for Image Recognition.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Deep Residual Learning for Image Recognition

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T12:43:35.540792Z

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Observation 066bdeb3-2b0c-4c15-b4f8-6df3c67263cf · outbound

This paper cites Investigating and Mitigating Failure Modes in Physics-informed Neural Networks (PINNs).

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Investigating and Mitigating Failure Modes in Physics-informed Neural Networks (PINNs)

Reference 34

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Observation e9705032-6e0b-4396-8b59-8ac801dfbfd8 · outbound

This paper cites Achieving High Accuracy with PINNs via Energy Natural Gradients.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Achieving High Accuracy with PINNs via Energy Natural Gradients

Reference 35

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Observation f5ac6786-ad7f-4da0-85a2-8ec5e65e21ba · outbound

This paper cites Improving Energy Natural Gradient Descent through Woodbury, Momentum, and Randomization, May 2025.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Improving Energy Natural Gradient Descent through Woodbury, Momentum, and Randomization, May 2025

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-04T12:43:35.991288Z

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Observation 81070f06-bff8-4068-8fbd-409667e881ed · outbound

This paper cites an unresolved cited work.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Unresolved cited work

Reference 37

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Observation d3ebcb9b-389d-4703-9f4b-5969ba450d67 · outbound

This paper cites From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-04T12:43:36.322205Z

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Observation 6130aeec-ed7b-472c-8308-2069210a03e2 · outbound

This paper cites Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-04T12:43:36.469540Z

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