Paper Citation Record · LEDGER

Case Studies of Generative Machine Learning Models for Dynamical Systems

As of 8 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 1 inbound Pith citation observation for arXiv:2508.04459.

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

pith.paper-citation-record.v1
2508.04459 v1

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:01:46.956921Z

measured 57 of 57 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-13T21:35:52.012244Z

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

56 of 56 outbound references displayed

verified exact16
verified fuzzy4
unresolved29
parse uncertain0
malformed identifier0
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External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 0733dc72-c7df-4f64-a958-872f3d1d1737 · outbound

This paper cites Optimal and Autonomous Control Using Reinforcement Learning: A Survey,.

Case Studies of Generative Machine Learning Models for Dynamical Systems Optimal and Autonomous Control Using Reinforcement Learning: A Survey,

Reference 1

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Observation 8e18242c-1d63-4072-9829-c0d4a37ab5a2 · outbound

This paper cites Asurveyofdeeplearningapplicationstoautonomousvehiclecontrol,.

Case Studies of Generative Machine Learning Models for Dynamical Systems Asurveyofdeeplearningapplicationstoautonomousvehiclecontrol,

Reference 2

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Observation 948a4110-61fb-403a-ac69-1e8cdc7e176d · outbound

This paper cites doi:10.2514/6.2022-2103, URLhttps://arc.aiaa.

Case Studies of Generative Machine Learning Models for Dynamical Systems doi:10.2514/6.2022-2103, URLhttps://arc.aiaa

Reference 3

Resolution: verified exact; doi, observed 2026-08-06T00:01:47.904041Z

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Observation 5abcc0ca-9885-4fd6-9799-bcffc96fc0f0 · outbound

This paper cites doi:10.2514/6.2023-1814, URLhttps://arc.aiaa.org/doi/abs/10.2514/6.2023-1814.

Case Studies of Generative Machine Learning Models for Dynamical Systems doi:10.2514/6.2023-1814, URLhttps://arc.aiaa.org/doi/abs/10.2514/6.2023-1814

Reference 4

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Observation 5800f567-e23d-45e0-9922-b4b3a6d8a199 · outbound

This paper cites 15 Sample outputs of the Split-VAE for 200 training samples for𝜆= 5.

Case Studies of Generative Machine Learning Models for Dynamical Systems 15 Sample outputs of the Split-VAE for 200 training samples for𝜆= 5

Reference 5

Resolution: verified exact; doi, observed 2026-08-06T00:01:47.885045Z

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Observation 14ea0521-1dee-464c-a3b8-0eac660f8fb8 · outbound

This paper cites 97 – 155.

Case Studies of Generative Machine Learning Models for Dynamical Systems 97 – 155

Reference 6

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Observation 008733a8-7415-42b8-a73e-6c4e85db3da1 · outbound

This paper cites an unresolved cited work.

Case Studies of Generative Machine Learning Models for Dynamical Systems Unresolved cited work

Reference 7

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Observation 7d75ec03-6aa4-4dac-bb32-8938c88cff92 · outbound

This paper cites doi:10.1137/1.9780898719376.

Case Studies of Generative Machine Learning Models for Dynamical Systems doi:10.1137/1.9780898719376

Reference 8

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Observation ead49ae2-19d6-4f13-8a53-08c9a787d971 · outbound

This paper cites Anintroductiontodeepreinforcementlearning,.

Case Studies of Generative Machine Learning Models for Dynamical Systems Anintroductiontodeepreinforcementlearning,

Reference 9

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Observation c5544d4e-9a20-4bca-a196-3e9393d581bc · outbound

This paper cites I.,Synthetic Data for Deep Learning, Springer Optimization and Its Applications, Springer, Cham, Switzerland,.

Case Studies of Generative Machine Learning Models for Dynamical Systems I.,Synthetic Data for Deep Learning, Springer Optimization and Its Applications, Springer, Cham, Switzerland,

Reference 10

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Observation d0747ba7-ef3c-46bb-b372-64080175dc78 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Case Studies of Generative Machine Learning Models for Dynamical Systems Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 11

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Observation 6c2d5cfc-d057-4167-8824-52c6be0c2394 · outbound

This paper cites AnempiricalevaluationofGitHubcopilot’scodesuggestions,.

Case Studies of Generative Machine Learning Models for Dynamical Systems AnempiricalevaluationofGitHubcopilot’scodesuggestions,

Reference 12

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Observation d92aaeab-9c4a-4ffb-8700-f3053c7ce691 · outbound

This paper cites Face generation and editing with stylegan: A survey,.

Case Studies of Generative Machine Learning Models for Dynamical Systems Face generation and editing with stylegan: A survey,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:01:48.913649Z

Source-reported events for the cited work

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Observation 8c29625a-b244-4c11-b469-503d08a09b72 · outbound

This paper cites Diffusion Policy: Visuomotor Policy Learning via Action Diffusion.

Case Studies of Generative Machine Learning Models for Dynamical Systems Diffusion Policy: Visuomotor Policy Learning via Action Diffusion

Reference 14

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Observation 6b97a998-60bf-46b1-8be4-11ce8f8ecb9b · outbound

This paper cites Diffusion Models for Generating Ballistic Spacecraft Trajectories.

Case Studies of Generative Machine Learning Models for Dynamical Systems Diffusion Models for Generating Ballistic Spacecraft Trajectories

Reference 15

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Observation 56ea3d5c-a943-432f-8277-68f84dcec80a · outbound

This paper cites An Example of Synthetic Data Generation for Control Systems using GenerativeAdversarialNetworks: ZermeloMinimum-TimeNavigation,.

Case Studies of Generative Machine Learning Models for Dynamical Systems An Example of Synthetic Data Generation for Control Systems using GenerativeAdversarialNetworks: ZermeloMinimum-TimeNavigation,

Reference 16

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Observation 7622cc68-b911-49da-a1a8-7efd6ade3c1b · outbound

This paper cites Multilayer feedforward networks are universal approximators,.

Case Studies of Generative Machine Learning Models for Dynamical Systems Multilayer feedforward networks are universal approximators,

Reference 17

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Observation 0a2ce1a8-4627-4343-ba6d-f5a427cc311f · outbound

This paper cites Multilayerfeedforwardnetworkswithanonpolynomialactivationfunction can approximate any function,.

Case Studies of Generative Machine Learning Models for Dynamical Systems Multilayerfeedforwardnetworkswithanonpolynomialactivationfunction can approximate any function,

Reference 18

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Observation 2fe1074b-8e11-4447-b7cb-510aa5b6142d · outbound

This paper cites Generative Adversarial Networks.

Case Studies of Generative Machine Learning Models for Dynamical Systems Generative Adversarial Networks

Reference 19

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Observation 4012013d-abcd-49b7-93b2-cc6b07180dda · outbound

This paper cites An Introduction to Variational Autoencoders.

Case Studies of Generative Machine Learning Models for Dynamical Systems An Introduction to Variational Autoencoders

Reference 20

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Observation fc3625ee-dcbb-4d7b-b272-ee2eb2ba046f · outbound

This paper cites A Brief Introduction to Generative Models.

Case Studies of Generative Machine Learning Models for Dynamical Systems A Brief Introduction to Generative Models

Reference 21

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Observation 1d8e6fc3-896d-45cf-be79-119c90a15681 · outbound

This paper cites Generative Adversarial Networks: An Overview.

Case Studies of Generative Machine Learning Models for Dynamical Systems Generative Adversarial Networks: An Overview

Reference 22

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Observation 2de779ae-92ad-4f11-b743-ec0b20e16d9a · outbound

This paper cites NVAE: A Deep Hierarchical Variational Autoencoder.

Case Studies of Generative Machine Learning Models for Dynamical Systems NVAE: A Deep Hierarchical Variational Autoencoder

Reference 23

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This paper cites TrajVAE:Avariationalautoencodermodelfortrajectorygeneration,.

Case Studies of Generative Machine Learning Models for Dynamical Systems TrajVAE:Avariationalautoencodermodelfortrajectorygeneration,

Reference 24

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Observation c714aeee-bb22-44de-b187-5fdc615dd117 · outbound

This paper cites Physically Interpretable Feature Learning of Supercritical Airfoils Based on Variational Autoencoders,.

Case Studies of Generative Machine Learning Models for Dynamical Systems Physically Interpretable Feature Learning of Supercritical Airfoils Based on Variational Autoencoders,

Reference 25

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Observation d24d859e-0071-4d75-b077-7e02af52b05e · outbound

This paper cites Anomaly detection for time series using vae-lstm hybrid model,.

Case Studies of Generative Machine Learning Models for Dynamical Systems Anomaly detection for time series using vae-lstm hybrid model,

Reference 26

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Observation e023884b-cb6e-49f6-889c-c664ab873a77 · outbound

This paper cites Collaborative variational deep learning for healthcare recommendation,.

Case Studies of Generative Machine Learning Models for Dynamical Systems Collaborative variational deep learning for healthcare recommendation,

Reference 27

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Observation 8abef148-4ee9-4d7a-b4b4-a904e75fa558 · outbound

This paper cites StyleHEAT: One-Shot High-Resolution Editable Talking Face Generation via Pre-trained StyleGAN.

Case Studies of Generative Machine Learning Models for Dynamical Systems StyleHEAT: One-Shot High-Resolution Editable Talking Face Generation via Pre-trained StyleGAN

Reference 28

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Observation 719b76ae-5362-46aa-856e-e65a09279bc7 · outbound

This paper cites Time-series Generative Adversarial Networks,.

Case Studies of Generative Machine Learning Models for Dynamical Systems Time-series Generative Adversarial Networks,

Reference 29

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Observation 586ac658-b207-4593-bb49-ca1918ed288b · outbound

This paper cites TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation.

Case Studies of Generative Machine Learning Models for Dynamical Systems TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation

Reference 30

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Observation 6a302aa6-e3fd-41c2-aa0d-3958746e6e60 · outbound

This paper cites RL-CycleGAN: Reinforcement Learning Aware Simulation-To-Real.

Case Studies of Generative Machine Learning Models for Dynamical Systems RL-CycleGAN: Reinforcement Learning Aware Simulation-To-Real

Reference 31

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Observation fcd34247-947d-440f-b346-4cc5ead1a847 · outbound

This paper cites Tutorial on Amortized Optimization,.

Case Studies of Generative Machine Learning Models for Dynamical Systems Tutorial on Amortized Optimization,

Reference 32

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This paper cites Semi-Amortized Variational Autoencoders.

Case Studies of Generative Machine Learning Models for Dynamical Systems Semi-Amortized Variational Autoencoders

Reference 33

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Observation bba5eba6-6947-477a-80c0-d18cdee68461 · outbound

This paper cites Iterative Amortized Inference.

Case Studies of Generative Machine Learning Models for Dynamical Systems Iterative Amortized Inference

Reference 34

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This paper cites DC3: A learning method for optimization with hard constraints.

Case Studies of Generative Machine Learning Models for Dynamical Systems DC3: A learning method for optimization with hard constraints

Reference 35

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Observation 8874bc7e-cc64-4f48-8fec-224ec7602b1f · outbound

This paper cites Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,.

Case Studies of Generative Machine Learning Models for Dynamical Systems Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,

Reference 36

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This paper cites nn-PINNs: Non-Newtonian physics-informed neural networks for complex fluid modeling,.

Case Studies of Generative Machine Learning Models for Dynamical Systems nn-PINNs: Non-Newtonian physics-informed neural networks for complex fluid modeling,

Reference 37

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This paper cites Physics-Informed Machine Learning and Uncertainty Quantification for Mechanics of Heterogeneous Materials.

Case Studies of Generative Machine Learning Models for Dynamical Systems Physics-Informed Machine Learning and Uncertainty Quantification for Mechanics of Heterogeneous Materials

Reference 38

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Observation 1ff17a77-612a-4abb-b701-500fde109e7d · outbound

This paper cites Multiple Case Physics-Informed Neural Network for Biomedical Tube Flows.

Case Studies of Generative Machine Learning Models for Dynamical Systems Multiple Case Physics-Informed Neural Network for Biomedical Tube Flows

Reference 39

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Observation 99e5a06d-0d8b-47e3-a300-82ba990fadc7 · outbound

This paper cites A physics-informed Transformer model for vehicle trajectory prediction on highways,.

Case Studies of Generative Machine Learning Models for Dynamical Systems A physics-informed Transformer model for vehicle trajectory prediction on highways,

Reference 40

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Observation b7093d81-1e15-46aa-988e-3b1e570cab66 · outbound

This paper cites Physics-informed Neural Networks to Model and Control Robots: a Theoretical and Experimental Investigation.

Case Studies of Generative Machine Learning Models for Dynamical Systems Physics-informed Neural Networks to Model and Control Robots: a Theoretical and Experimental Investigation

Reference 41

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Observation 7a437b8b-45b9-45d1-8013-7b7608f6cae8 · outbound

This paper cites Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations.

Case Studies of Generative Machine Learning Models for Dynamical Systems Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-08-06T00:01:47.083520Z

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Observation b37c75c4-0d56-4eb8-b61d-a5f927f89f4c · outbound

This paper cites Generative Ensemble Regression: Learning Particle Dynamics from Observations of Ensembles with Physics-Informed Deep Generative Models.

Case Studies of Generative Machine Learning Models for Dynamical Systems Generative Ensemble Regression: Learning Particle Dynamics from Observations of Ensembles with Physics-Informed Deep Generative Models

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-08-06T00:01:47.069543Z

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Observation 661795e3-da24-421c-a029-6f3f471ba490 · outbound

This paper cites Navier–stokes generative adversarial network: A physics-informed deep learning model for fluid flow generation,.

Case Studies of Generative Machine Learning Models for Dynamical Systems Navier–stokes generative adversarial network: A physics-informed deep learning model for fluid flow generation,

Reference 44

Resolution: verified exact; doi, observed 2026-08-06T00:01:47.052807Z

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Observation 3db7ef23-1279-4052-9c58-eda2f1caedff · outbound

This paper cites Hierarchical dynamic wake modeling of wind turbine based on physics-informed generative deep learning,.

Case Studies of Generative Machine Learning Models for Dynamical Systems Hierarchical dynamic wake modeling of wind turbine based on physics-informed generative deep learning,

Reference 45

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T00:01:48.192761Z

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Observation db94464e-015f-47cc-a15b-0fa1cd3c20ed · outbound

This paper cites E., and Ho, Y.-C.,Applied optimal control: optimization, estimation and control, Taylor & Francis, New York, NY, USA, 1975.

Case Studies of Generative Machine Learning Models for Dynamical Systems E., and Ho, Y.-C.,Applied optimal control: optimization, estimation and control, Taylor & Francis, New York, NY, USA, 1975

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:01:48.890695Z

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Observation 3236d807-7b2d-4f2f-9761-4aff25e78c05 · outbound

This paper cites Tackling mode collapse in multi-generator GANs with orthogonal vectors,.

Case Studies of Generative Machine Learning Models for Dynamical Systems Tackling mode collapse in multi-generator GANs with orthogonal vectors,

Reference 47

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Observation 6c21c3de-7814-4536-aa12-97fc0d7eee4f · outbound

This paper cites Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations.

Case Studies of Generative Machine Learning Models for Dynamical Systems Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-08-06T00:01:47.042940Z

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Observation 80eb0956-01da-4f6d-966d-79f6b55f83ac · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

Case Studies of Generative Machine Learning Models for Dynamical Systems PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 49

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This paper cites An Empirical Study on Generalizations of the ReLU Activation Function,.

Case Studies of Generative Machine Learning Models for Dynamical Systems An Empirical Study on Generalizations of the ReLU Activation Function,

Reference 50

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Observation a4c27258-0a2d-4197-8070-ca137b19a81b · outbound

This paper cites Animagedifferencemetricbasedonsimulationofimagedetailvisibilityandtotalvariation,.

Case Studies of Generative Machine Learning Models for Dynamical Systems Animagedifferencemetricbasedonsimulationofimagedetailvisibilityandtotalvariation,

Reference 51

Resolution: verified exact; doi, observed 2026-08-06T00:01:47.019749Z

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Observation faf57d1b-c606-42b8-8af6-a6515c4e901b · outbound

This paper cites Understanding GANs: Fundamentals, variants, training challenges, applications, and open problems,.

Case Studies of Generative Machine Learning Models for Dynamical Systems Understanding GANs: Fundamentals, variants, training challenges, applications, and open problems,

Reference 52

Resolution: verified exact; doi, observed 2026-08-06T00:01:47.009955Z

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Observation 34f99ce0-bc2e-484d-8ebc-1312c31e4719 · outbound

This paper cites Training Generative Adversarial Networks with Limited Data.

Case Studies of Generative Machine Learning Models for Dynamical Systems Training Generative Adversarial Networks with Limited Data

Reference 53

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This paper cites BEGANv3: avoidingmodecollapseinGANsusingvariationalinference,.

Case Studies of Generative Machine Learning Models for Dynamical Systems BEGANv3: avoidingmodecollapseinGANsusingvariationalinference,

Reference 54

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This paper cites an unresolved cited work.

Case Studies of Generative Machine Learning Models for Dynamical Systems Unresolved cited work

Reference 2021

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Reference 2024

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Pith citing papers

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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 Case Studies of Generative Machine Learning Models for Dynamical Systems

Reference 30

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