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Paper Citation Record · LEDGER

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network

As of 15 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2412.17978.

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

pith.paper-citation-record.v1
2412.17978 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T05:12:33.203552Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

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  • verified fuzzy32
  • unresolved6
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6e381245-defe-400f-bcb6-b120c7879ba4 · outbound

This paper cites , " * write output.state after.block = add.period write newline.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network , " * write output.state after.block = add.period write newline

Reference 1

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 540cb419-e0c4-496c-8fcc-7363cc0e2fd4 · outbound

This paper cites write newline.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network write newline

Reference 2

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no resolver link, observed 2026-08-11T05:12:33.056007Z

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source=arxiv_source observed=2026-08-11T05:12:33.056007Z digest=sha256:a908a8b2d7a6e53c8c74da088ce9f8189adcee02a023467039292e3e698a6d1a

Observation 80d69706-9676-45bb-92b8-515369cecc47 · outbound

This paper cites Structural Control and Health Monitoring 24 (3), e1889.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network Structural Control and Health Monitoring 24 (3), e1889

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation cbbe1858-3914-41d6-8c25-df52aa031853 · outbound

This paper cites Computational Mechanics 64 (2), 525--545.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network Computational Mechanics 64 (2), 525--545

Reference 4

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T05:12:33.064468Z digest=sha256:53e983077d5823219ce4d55e62bd989092a043e8f43b1be6d0ceb44af5fc3412

Observation a7d985ac-d34a-4a90-a8e6-6e124fb8c457 · outbound

This paper cites Computers & fluids 35 (3), 326--348.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network Computers & fluids 35 (3), 326--348

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T05:12:33.069412Z digest=sha256:fe77cd2cd4fbfafa9cb4557cebd2c805f8d8c7d0e3126fc2e054e6c13f75b58b

Observation 9887e6f4-a20a-41de-a3de-306aaa9b228d · outbound

This paper cites Proceedings of the national academy of sciences 113 (15), 3932--3937.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network Proceedings of the national academy of sciences 113 (15), 3932--3937

Reference 6

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation f7f4740f-6188-46cc-a8c8-b2388023c999 · outbound

This paper cites IEEE Transactions on Power Systems.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network IEEE Transactions on Power Systems

Reference 7

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T05:12:33.077425Z digest=sha256:cf830ecf46a146a95bf5b332d448bc4db2d6d50a4a2d8d5ac8bb50bc620a567f

Observation afa1d998-f8c5-4c89-be72-a5ba2b24fc48 · outbound

This paper cites In 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI)\/ , pp.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network In 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI)\/ , pp

Reference 8

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation b1d7d968-4fa6-4ff2-92f6-42abb279a04e · outbound

This paper cites Computer Methods in Applied Mechanics and Engineering 365 , 113000.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network Computer Methods in Applied Mechanics and Engineering 365 , 113000

Reference 9

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T05:12:33.085167Z digest=sha256:f4a5c8a1315ca316443f9ee23e12babb7511db62719b592090ebf7e4b3d709e2

Observation 3f5827f1-f89c-4c3b-824c-dc2b59d22ff3 · outbound

This paper cites The Annals of Statistics pp.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network The Annals of Statistics pp

Reference 10

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T05:12:33.089100Z digest=sha256:74544ad674063b3993dc10053202b14ff40d98065e635e865e80d0ae73eb8f10

Observation 8616e217-fe03-4da5-aacb-9734beaf09d3 · outbound

This paper cites Physics of Fluids 31 (12), 125111.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network Physics of Fluids 31 (12), 125111

Reference 11

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T05:12:33.092796Z digest=sha256:8eac2e4f04a8012f624e51a8362e79e8bcbc09aa409885dd05c704e715423514

Observation b6cb4b02-1909-4cd6-b155-5ef6d80a8cdd · outbound

This paper cites Strain 55 (1), e12297.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network Strain 55 (1), e12297

Reference 12

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 6a833cb1-e40f-43fc-9a06-6f05d4449816 · outbound

This paper cites Deep Learning the Physics of Transport Phenomena.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network Deep Learning the Physics of Transport Phenomena

Reference 13

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unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T05:12:33.100841Z digest=sha256:7a38ccf6758300be4947d61e0bed68ed1c6c0a6842b2b1a13aa3a3fade53306e

Observation cdd99360-a3b8-43f8-9221-4ac082a66962 · outbound

This paper cites Multi-fidelity Generative Deep Learning Turbulent Flows.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network Multi-fidelity Generative Deep Learning Turbulent Flows

Reference 14

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T05:12:33.105165Z digest=sha256:7bdaa736e59867fa02dc0c5c96e535011b883b5b9ee122977efb9379df2cc489

Observation fc67d536-56eb-4686-ae24-ae0047390b23 · outbound

This paper cites In Proceedings of the thirteenth international conference on artificial intelligence and statistics\/ , pp.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network In Proceedings of the thirteenth international conference on artificial intelligence and statistics\/ , pp

Reference 15

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T05:12:33.109173Z digest=sha256:e09420b486ca40c886f5ed6aef497fb771d33e0c70d5d0812cef1e050ab3338b

Observation 473d7e82-116b-4562-8485-9255ad78acad · outbound

This paper cites Communications of the ACM 63 (11), 139--144.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network Communications of the ACM 63 (11), 139--144

Reference 16

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 2536d881-d34b-4b4f-aff7-9b91f12d3a88 · outbound

This paper cites In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining\/ , pp.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining\/ , pp

Reference 17

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 417d2c33-f242-4047-83a7-fdaeaebce14d · outbound

This paper cites Computer Methods in Applied Mechanics and Engineering 318 , 382--411.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network Computer Methods in Applied Mechanics and Engineering 318 , 382--411

Reference 18

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T05:12:33.119698Z digest=sha256:fbe93286d7601bd54e116f2616c30457a84df88a007e51eb986c78b6482e209d

Observation 48e11fa7-1e04-4977-90e6-1052cfe68905 · outbound

This paper cites In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)\/ , pp.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)\/ , pp

Reference 19

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation a044ba75-d5bf-4bc0-b3e7-20fff6547db9 · outbound

This paper cites Proceedings of the Royal Society A 476 (2242), 20200279.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network Proceedings of the Royal Society A 476 (2242), 20200279

Reference 20

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T05:12:33.126981Z digest=sha256:5dbee220bc90bbe9dd66fc6c20a0debcb97eae4e05a698d6683cfe45fb50e011

Observation c7e9b9b1-aa65-46ef-ab67-710f76a0ab2e · outbound

This paper cites Auto-Encoding Variational Bayes.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network Auto-Encoding Variational Bayes

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-11T05:12:33.130480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T05:12:33.130480Z digest=sha256:fe113cab33e84290de30af530a068130a3b50ac54972882fa8580d947d7ca474

Observation bd319b5f-27e0-4f3b-990f-7424ff75b96f · outbound

This paper cites Physical Review E 100 (2), 022220.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network Physical Review E 100 (2), 022220

Reference 22

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T05:12:33.134155Z digest=sha256:2f266acea14f45a210ccf2a16de800dcbe600e7d0243b68e2c069868140f305e

Observation c0d1696b-44d4-4214-bec2-054fad009c2f · outbound

This paper cites Journal of Wind Engineering and Industrial Aerodynamics 172 , 196--211.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network Journal of Wind Engineering and Industrial Aerodynamics 172 , 196--211

Reference 23

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T05:12:33.137644Z digest=sha256:df7fe92c828cc44d6eff4f880c160f70b05fd85e1d829ddac6ff66fa62fa050f

Observation 7db19dba-80c7-4f11-a328-e094f96c467c · outbound

This paper cites Engineering Structures 155 , 1--15.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network Engineering Structures 155 , 1--15

Reference 24

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T05:12:33.141436Z digest=sha256:ac67114627e93b2d13728b1041989f5701309e3034744a650d24ee0982df0ad3

Observation 94a76758-b897-48d9-a47e-7e07ae1054b6 · outbound

This paper cites Nonlinear Dynamics 106 (4), 3231--3246.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network Nonlinear Dynamics 106 (4), 3231--3246

Reference 25

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T05:12:33.145557Z digest=sha256:983c91f9cc72210c740d62dd9047ba9a9e800110a6968569fe719a80597cbd04

Observation 3c75cdd3-d3c9-4821-8775-bc93725bf482 · outbound

This paper cites Nonlinear Dynamics 105 (4), 3409--3422.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network Nonlinear Dynamics 105 (4), 3409--3422

Reference 26

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T05:12:33.149316Z digest=sha256:2fea6c67222eed529b52a8721c194a5f0df38da46535863131e1ea6ca6171f27

Observation 549859db-c885-45ae-aa67-eb0d3306e609 · outbound

This paper cites Nature communications 9 (1), 1--10.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network Nature communications 9 (1), 1--10

Reference 27

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T05:12:33.152873Z digest=sha256:d14f24ddde54f96c874d4e21530cfe221e2412f6ba410b19ac9b60bb9dbec6f8

Observation 43907384-3db1-4aa0-bb36-1ea35fc7475f · outbound

This paper cites Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders

Reference 28

Resolution
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local_arxiv, observed 2026-08-11T05:12:33.282890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T05:12:33.156640Z digest=sha256:3149a986886926749cd3f92181b8dd920bc42190f8b02e58d87c4136a2d72153

Observation 4f4a1b0a-4fd4-48dd-b504-209d58e8c643 · outbound

This paper cites Computers & Fluids 32 (3), 337--352.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network Computers & Fluids 32 (3), 337--352

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T05:12:33.441290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T05:12:33.160429Z digest=sha256:efa30856da68a4ea9f35ab287bea3ff98d2dac2d4f5ee4d3ef04b1eda96e94a2

Observation 2859fc93-1471-41d5-b2ab-8f8a05df5713 · outbound

This paper cites Chaos: An Interdisciplinary Journal of Nonlinear Science 28 (6), 063116.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network Chaos: An Interdisciplinary Journal of Nonlinear Science 28 (6), 063116

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T05:12:33.429625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T05:12:33.163704Z digest=sha256:817ec02e4b7119597e6cc47df5b874920a5dcf0fc861017da9eeca1699d72582

Observation 639eeb4e-fab3-4766-ad5f-a870a3cc8495 · outbound

This paper cites Science 367 (6481), 1026--1030.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network Science 367 (6481), 1026--1030

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-11T05:12:33.167170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T05:12:33.167170Z digest=sha256:8167fff0ffa6421f6dba2aa7c96496391b2dda51242afecf03c470c480bede32

Observation c9054d3d-ed70-49d4-9d4b-54373b88468e · outbound

This paper cites IEEE Transactions on Power Systems 34 (6), 5044--5052.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network IEEE Transactions on Power Systems 34 (6), 5044--5052

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T05:12:33.411385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T05:12:33.170592Z digest=sha256:a7be015d86fbebd05613db2767004edaf0a5be5b620f83d19e6edc7a62d5115c

Observation d5d32804-cdcd-4b28-8ab2-127c5bd73fc1 · outbound

This paper cites Robotics and Autonomous Systems 124 , 103386.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network Robotics and Autonomous Systems 124 , 103386

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T05:12:33.399727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T05:12:33.174034Z digest=sha256:313aea7fca417f22af16d9459e434ca9d7f4b89662fa07bf09261a9c488539d2

Observation d2bb5070-fed3-4c45-987f-e1cf51983f5d · outbound

This paper cites Journal of Design Studio 6 (2), 325--335.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network Journal of Design Studio 6 (2), 325--335

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T05:12:33.387988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T05:12:33.177997Z digest=sha256:a471cbf0489b9e1a227d97923c528e9e90f2a0e2fc1a4ad87e2fd24dd1bf60ae

Observation 068ba817-b8ed-499b-aec4-a52a1560ba51 · outbound

This paper cites Journal of Design Studio 6 (2), 383--395.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network Journal of Design Studio 6 (2), 383--395

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T05:12:33.375760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T05:12:33.181684Z digest=sha256:241941da18de6dfe587cea6a07a06d0a5e6ac1cde191da9dea1935d85c4b2fef

Observation 52e6ce0e-84c9-4192-9b1c-86b61d79b71b · outbound

This paper cites Machine Learning Approach to Model Order Reduction of Nonlinear Systems via Autoencoder and LSTM Networks.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network Machine Learning Approach to Model Order Reduction of Nonlinear Systems via Autoencoder and LSTM Networks

Reference 36

Resolution
metadata mismatch
local_arxiv, observed 2026-08-11T05:12:33.264585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T05:12:33.185497Z digest=sha256:c5cfd9b2b582f74ec6eeb89570f2beb20583e8f20a6cb9b50be962bc2d0083a6

Observation 5fec07ff-c0c1-45a2-84db-d4edd3cdcea2 · outbound

This paper cites Data-Driven, Physics-Based Feature Extraction from Fluid Flow Fields.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network Data-Driven, Physics-Based Feature Extraction from Fluid Flow Fields

Reference 37

Resolution
metadata mismatch
local_arxiv, observed 2026-08-11T05:12:33.245965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T05:12:33.189283Z digest=sha256:edc45cb13e2342bdd648613b8543bda9ac614f414364d3343f022297f5f6c5c9

Observation 4ce0071b-ccee-4f93-855d-b7979f0b5226 · outbound

This paper cites Mechanical Systems and Signal Processing 166 , 108473.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network Mechanical Systems and Signal Processing 166 , 108473

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T05:12:33.363962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T05:12:33.193075Z digest=sha256:4fa2978ad8c4290a622c8a2ad65151b4712ad91deb169bb4a0d3d88e342d993e

Observation 26c14936-077c-4ca1-9f27-ae5244212dbd · outbound

This paper cites In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining\/ , pp.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining\/ , pp

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T05:12:33.352350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T05:12:33.196591Z digest=sha256:f92afed5f2341c3a3da25775389299231fd1d605b0ef388f82a9ee40ade8031e

Observation eeb857b3-1d91-4518-8f23-db3ae983cff1 · outbound

This paper cites Mechanical Systems and Signal Processing 84 , 34--53.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network Mechanical Systems and Signal Processing 84 , 34--53

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T05:12:33.340677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T05:12:33.200007Z digest=sha256:bd570f657a496a775cb1a18ccec82e8d58fc1591c8d4947919ba812b0c23db4f

Observation 70f56585-db3c-44e7-afb1-f058f61f6cbc · outbound

This paper cites ACM Transactions on Graphics (TOG) 37 (4), 1--15.

Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network ACM Transactions on Graphics (TOG) 37 (4), 1--15

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T05:12:33.328935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-11T05:12:33.203552Z digest=sha256:e8a610e9511fc64862fbea29b52c43ffdd1390d47740cf267eee3e066a6aea14

Pith citing papers

No inbound Pith citation observations are available.