Paper Citation Record · LEDGER

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study

As of 20 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2506.05860.

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

pith.paper-citation-record.v1
2506.05860 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:20:17.510061Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

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

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

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External citation measurements

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Outbound references

Observation 98c7874f-de91-4294-8812-9a0ce4e8654f · outbound

This paper cites Roters, P.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Roters, P

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:18.106278Z

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Observation f29bf3c4-429f-4b2f-a28f-a0440037bd11 · outbound

This paper cites Dassault Systémes Simulia Corp, United States, 2009.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Dassault Systémes Simulia Corp, United States, 2009

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:18.092431Z

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Observation 128b0f8b-eba8-4d39-97e3-44a7302ce199 · outbound

This paper cites Langer, E.R.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Langer, E.R

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:18.077901Z

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Observation 9b8a0a6a-f917-4aa4-a411-a31f909398fc · outbound

This paper cites Fromm, Kunok Chang, David L.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Fromm, Kunok Chang, David L

Reference 4

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Observation 74da48b4-d2d6-4e21-a3b0-6445d305c00e · outbound

This paper cites an unresolved cited work.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Unresolved cited work

Reference 5

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Observation 4e0718e7-436c-4261-a356-209c86713c43 · outbound

This paper cites an unresolved cited work.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:20:18.034289Z

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Observation 14b577ef-9b65-465f-8a66-412ac4d773b5 · outbound

This paper cites Groeber and Michael A.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Groeber and Michael A

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:18.019533Z

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Observation 0968e94f-a0d2-4617-b9f2-2d956552c4cd · outbound

This paper cites Mukherjee, T.A.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Mukherjee, T.A

Reference 8

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Observation a715017c-9e4b-407e-bd41-768840fa04a9 · outbound

This paper cites Mukherjee, T.A.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Mukherjee, T.A

Reference 9

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Observation 1ad425c6-4239-44ba-b86f-1cf5d7f89452 · outbound

This paper cites Gururajan and T.A.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Gururajan and T.A

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.975878Z

Source-reported events for the cited work

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Observation e61d9b5f-d9a2-4ea8-b1fc-52b1ea443bb3 · outbound

This paper cites Effect of strong nonuniformity in grain bound- ary energy on 3-D grain growth behavior: A phase- field simulation study.Computational Materials Science, 127:67–77, 2017.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Effect of strong nonuniformity in grain bound- ary energy on 3-D grain growth behavior: A phase- field simulation study.Computational Materials Science, 127:67–77, 2017

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.961605Z

Source-reported events for the cited work

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Observation 6c0cbbd9-8691-4859-bdbc-8c2ac15d449b · outbound

This paper cites Verma and R.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Verma and R

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.946261Z

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Observation e0376e6f-2e92-46b6-81a9-6b60564d24cc · outbound

This paper cites an unresolved cited work.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:20:17.931702Z

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Observation 304f921b-2f73-4ce4-8c62-9bbf82ea9846 · outbound

This paper cites an unresolved cited work.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Unresolved cited work

Reference 14

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Observation 9f8dda3b-2017-4b80-aae5-3e6ceae6fe68 · outbound

This paper cites Phase-field simulation for the evolu- tion of solid/liquid interface front in directional solidifica- tion process.Journal of Materials Science&Technology, 35(6):1044–1052, 2019.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Phase-field simulation for the evolu- tion of solid/liquid interface front in directional solidifica- tion process.Journal of Materials Science&Technology, 35(6):1044–1052, 2019

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.903158Z

Source-reported events for the cited work

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Observation 5e3b1050-ec3a-430c-9c9e-4e774f9b88b5 · outbound

This paper cites an unresolved cited work.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:20:17.888436Z

Source-reported events for the cited work

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Observation 471edf11-05b1-421e-bc7b-8251f8c367d6 · outbound

This paper cites Effect of co-existing external fields on a binary spin- odal system: A phase-field study.Journal of Physics and Chemistry of Solids, 132:236–243, 2019.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Effect of co-existing external fields on a binary spin- odal system: A phase-field study.Journal of Physics and Chemistry of Solids, 132:236–243, 2019

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.873028Z

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Observation 6045a4c0-14c1-4eb1-8c83-62b284b58b89 · outbound

This paper cites Mushongera, and Kumar Ankit.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Mushongera, and Kumar Ankit

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.858288Z

Source-reported events for the cited work

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Observation e51be5bc-f5ba-470a-b518-9e8d1f9269e9 · outbound

This paper cites Parallel computing for phase-field models.The International journal of high performance computing applications, 28(1):61–72, 2014.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Parallel computing for phase-field models.The International journal of high performance computing applications, 28(1):61–72, 2014

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.843245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 79c5d3c4-a875-4167-830c-b7c706791871 · outbound

This paper cites Murdock, Steven K.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Murdock, Steven K

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.827884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 69f81c14-4fda-4380-a8b1-e29ea1e1dd0f · outbound

This paper cites Physics-embedded graph network for accelerating phase- field simulation of microstructure evolution in additive manufacturing.npj Computational Materials, 8(1):201, 2022.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Physics-embedded graph network for accelerating phase- field simulation of microstructure evolution in additive manufacturing.npj Computational Materials, 8(1):201, 2022

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.813865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2d4c1a1f-67c4-42eb-a592-ed376c1b192f · outbound

This paper cites Learn- ing two-phase microstructure evolution using neural op- erators and autoencoder architectures.npj Computational Materials, 8(1):190, 2022.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Learn- ing two-phase microstructure evolution using neural op- erators and autoencoder architectures.npj Computational Materials, 8(1):190, 2022

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.799716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c3528495-3844-4226-acea-fcc74c6e9c3e · outbound

This paper cites an unresolved cited work.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:20:17.785597Z

Source-reported events for the cited work

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Observation 07386777-b1ba-4e25-8eee-c83338560942 · outbound

This paper cites an unresolved cited work.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:20:17.770707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ad1cbf2c-194d-438a-8492-eac2048bcbc6 · outbound

This paper cites Accelerating phase-field-based mi- crostructure evolution predictions via surrogate models trained by machine learning methods.npj Computational Materials, 7(1):1–11, 2021.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Accelerating phase-field-based mi- crostructure evolution predictions via surrogate models trained by machine learning methods.npj Computational Materials, 7(1):1–11, 2021

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.756783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2963d8f2-ceb0-41a5-899e-e50a9b040480 · outbound

This paper cites Time series forecasting of multiphase microstructure evo- lution using deep learning.Computational Materials Sci- ence, 247:113518, 2025.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Time series forecasting of multiphase microstructure evo- lution using deep learning.Computational Materials Sci- ence, 247:113518, 2025

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.742189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 27197314-f975-44a6-a64b-472bf743b782 · outbound

This paper cites Accelerating microstructure mod- eling via machine learning: A method combining autoen- coder and convlstm.Phys.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Accelerating microstructure mod- eling via machine learning: A method combining autoen- coder and convlstm.Phys

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.727216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1c729217-efaa-4eb7-b7bc-927ea2f71a85 · outbound

This paper cites Integrated phase field and ma- chine learning study of microstructure evolution during interface-controlled spinodal decomposition.Solid State Phenomena, 357:101–106, 6 2024.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Integrated phase field and ma- chine learning study of microstructure evolution during interface-controlled spinodal decomposition.Solid State Phenomena, 357:101–106, 6 2024

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.712300Z

Source-reported events for the cited work

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Observation 127e30d4-fb7b-4b81-93b4-14f118fda195 · outbound

This paper cites Generative artificial intelligence: Analyzing its future applications in additive manufacturing.Big Data and Cognitive Computing, 8(7), 2024.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Generative artificial intelligence: Analyzing its future applications in additive manufacturing.Big Data and Cognitive Computing, 8(7), 2024

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.697293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 752521dd-5990-454c-8855-15133ee59ae3 · outbound

This paper cites Generative artificial intelligence and its applications in materials science: Current situation and future perspectives.Journal of Materiomics, 9(4):798– 816, 2023.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Generative artificial intelligence and its applications in materials science: Current situation and future perspectives.Journal of Materiomics, 9(4):798– 816, 2023

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.683195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 23ec6c98-715a-46c8-92a2-8162cb4cf6d3 · outbound

This paper cites an unresolved cited work.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:20:17.668568Z

Source-reported events for the cited work

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Observation 96754c9e-396a-4b3e-b72b-a32e89c1ff79 · outbound

This paper cites Free Energy of a Nonuniform System.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Free Energy of a Nonuniform System

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.655246Z

Source-reported events for the cited work

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Observation 723f7b6a-6cfb-4c0c-ba73-7947cab1887b · outbound

This paper cites On spinodal decomposition.Acta metal- lurgica, 9(9):795–801, 1961.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study On spinodal decomposition.Acta metal- lurgica, 9(9):795–801, 1961

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.641422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1d1332e1-0fc5-42eb-86ac-be1a41ef8d13 · outbound

This paper cites Generative adversarial networks-based syn- thetic microstructures for data-driven materials design.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Generative adversarial networks-based syn- thetic microstructures for data-driven materials design

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.626048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 19cb9734-388c-4828-abbd-d8655aa35565 · outbound

This paper cites an unresolved cited work.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:20:17.609702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3d936859-92ba-40e0-9404-829afaab4857 · outbound

This paper cites Predic- tive microstructure image generation using denoising dif- fusion probabilistic models.Acta Materialia, 261:119406, 2023.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Predic- tive microstructure image generation using denoising dif- fusion probabilistic models.Acta Materialia, 261:119406, 2023

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.595020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 61cb8baf-7762-455f-82e9-82ff602530d8 · outbound

This paper cites Fritz, D.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Fritz, D

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.580312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e9ea9310-e45a-4bf5-8b87-c4690795eb6d · outbound

This paper cites Plane strain compression test and simple shear test of sin- gle crystal pure iron.Procedia Engineering, 81:1342– 1347, 2014.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Plane strain compression test and simple shear test of sin- gle crystal pure iron.Procedia Engineering, 81:1342– 1347, 2014

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.565090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 757e2779-6ecb-4f14-af57-7ccbc5ec4219 · outbound

This paper cites Hertzberg, R.P.

Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study Hertzberg, R.P

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.548454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.