Paper Citation Record · LEDGER

Deep Generative Methods and Tire Architecture Design

As of 9 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2507.11639.

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

pith.paper-citation-record.v1
2507.11639 v2

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:12:07.304392Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

37 of 37 outbound references displayed

verified exact1
verified fuzzy20
unresolved16
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fecd1d7b-598b-4789-8b9c-58a70dfe3ca1 · outbound

This paper cites Auto-encoding variational bayes,.

Deep Generative Methods and Tire Architecture Design Auto-encoding variational bayes,

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be42232f-a5a2-4a66-908a-7c78dd1b9dcb · outbound

This paper cites Generative adversarial nets,.

Deep Generative Methods and Tire Architecture Design Generative adversarial nets,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:04.235067Z

Source-reported events for the cited work

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Observation 6ebfbcf0-aa99-472f-9f0e-9c6b0cda6072 · outbound

This paper cites Denoising diffusion probabilistic models,.

Deep Generative Methods and Tire Architecture Design Denoising diffusion probabilistic models,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:04.279054Z

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Observation 32c8029a-b47f-402d-a161-d44e9de6516b · outbound

This paper cites ShapeNet: An Information-Rich 3D Model Repository.

Deep Generative Methods and Tire Architecture Design ShapeNet: An Information-Rich 3D Model Repository

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:04.338570Z

Source-reported events for the cited work

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Observation 582c3f83-ee95-4105-8cca-2417aa70ae65 · outbound

This paper cites Mmvae+: Enhancing the generative quality of multimodal vaes without compromises,.

Deep Generative Methods and Tire Architecture Design Mmvae+: Enhancing the generative quality of multimodal vaes without compromises,

Reference 5

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Source-reported events for the cited work

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Observation 8893e956-fdaa-4186-b0d2-8e61d5dd8bfc · outbound

This paper cites Argmax flows and multinomial diffusion: Learning categorical distributions,.

Deep Generative Methods and Tire Architecture Design Argmax flows and multinomial diffusion: Learning categorical distributions,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:11.122756Z

Source-reported events for the cited work

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Observation 0eaa5527-c886-4d2d-ab51-dab8fb8bd38f · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium,.

Deep Generative Methods and Tire Architecture Design Gans trained by a two time-scale update rule converge to a local nash equilibrium,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:04.539637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bbadce37-bf61-454e-8567-dc86f0c48537 · outbound

This paper cites Towards high-fidelity cfd on the cloud for the automotive and motorsport sectors,.

Deep Generative Methods and Tire Architecture Design Towards high-fidelity cfd on the cloud for the automotive and motorsport sectors,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:10.897237Z

Source-reported events for the cited work

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Observation 88d20943-4870-4e10-887d-8ffd36c26f81 · outbound

This paper cites Modeling and validation of a passenger car tire using finite element analysis,.

Deep Generative Methods and Tire Architecture Design Modeling and validation of a passenger car tire using finite element analysis,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:10.663634Z

Source-reported events for the cited work

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Observation de35fcdf-03f3-425f-ac7e-35cf62e536f9 · outbound

This paper cites Comparison of optimization algorithms for aerodynamic shape design,.

Deep Generative Methods and Tire Architecture Design Comparison of optimization algorithms for aerodynamic shape design,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:10.476314Z

Source-reported events for the cited work

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Observation 962c08af-0860-40f6-8b33-16460d59ebb7 · outbound

This paper cites Efficient global optimiza- tion of expensive black-box functions,.

Deep Generative Methods and Tire Architecture Design Efficient global optimiza- tion of expensive black-box functions,

Reference 11

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Source-reported events for the cited work

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Observation fb4be868-873b-4be7-bc04-c378c2e138fd · outbound

This paper cites an unresolved cited work.

Deep Generative Methods and Tire Architecture Design Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:12:10.300342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f8523f1b-96b3-4444-9e14-6a7f2602bc00 · outbound

This paper cites Devel- opment of a conditional generative adversarial network for airfoil shape optimization,.

Deep Generative Methods and Tire Architecture Design Devel- opment of a conditional generative adversarial network for airfoil shape optimization,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:10.121872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 30c7392e-96a8-49af-8b72-87c687226ba4 · outbound

This paper cites Generating various airfoils with required lift coefficients by combining naca and joukowski airfoils using conditional variational autoencoders,.

Deep Generative Methods and Tire Architecture Design Generating various airfoils with required lift coefficients by combining naca and joukowski airfoils using conditional variational autoencoders,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:09.951787Z

Source-reported events for the cited work

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Observation 54cdf0d6-d400-4118-b2a7-fe121aa84403 · outbound

This paper cites Variational autoencoder- based topological optimization of an anechoic coating: An efficient- and neural network-based design,.

Deep Generative Methods and Tire Architecture Design Variational autoencoder- based topological optimization of an anechoic coating: An efficient- and neural network-based design,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:09.745370Z

Source-reported events for the cited work

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Observation 8c2420fc-ac88-43ae-b10f-7dbc19d451d9 · outbound

This paper cites A Binded VAE for Inorganic Material Generation.

Deep Generative Methods and Tire Architecture Design A Binded VAE for Inorganic Material Generation

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:12:07.519624Z

Source-reported events for the cited work

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Observation 1c0bee27-ffda-4c6e-a72a-ff6c863ca911 · outbound

This paper cites A meta- vae for multi-component industrial systems generation,.

Deep Generative Methods and Tire Architecture Design A meta- vae for multi-component industrial systems generation,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:05.556824Z

Source-reported events for the cited work

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Observation 36da9c2e-cdae-4fc5-a32f-8ca37dd7f00d · outbound

This paper cites A survey of multimodal deep generative models,.

Deep Generative Methods and Tire Architecture Design A survey of multimodal deep generative models,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:09.572705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 80e42f14-ab16-4d4b-9ca1-6cd6769aef72 · outbound

This paper cites Multimodal generative models for scalable weakly-supervised learning,.

Deep Generative Methods and Tire Architecture Design Multimodal generative models for scalable weakly-supervised learning,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:05.796343Z

Source-reported events for the cited work

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Observation cc2dcea9-6c3c-4d4d-8881-d90330cc5670 · outbound

This paper cites Variational mixture-of-experts autoen- coders for multi-modal deep generative models,.

Deep Generative Methods and Tire Architecture Design Variational mixture-of-experts autoen- coders for multi-modal deep generative models,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:05.890158Z

Source-reported events for the cited work

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Observation 6aef36f7-7b7c-420d-98dd-e0cad727593e · outbound

This paper cites Multimodal generative learning utilizing jensen-shannon-divergence,.

Deep Generative Methods and Tire Architecture Design Multimodal generative learning utilizing jensen-shannon-divergence,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:05.948466Z

Source-reported events for the cited work

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Observation 1af085d9-f780-4ff0-9993-bb0019360aeb · outbound

This paper cites Generalized Multimodal ELBO.

Deep Generative Methods and Tire Architecture Design Generalized Multimodal ELBO

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:06.070211Z

Source-reported events for the cited work

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Observation 9f1377ce-8d8d-4b7d-887b-901023ef37b8 · outbound

This paper cites Material microstructure design using vae-regression with a multimodal prior,.

Deep Generative Methods and Tire Architecture Design Material microstructure design using vae-regression with a multimodal prior,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:09.363075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f648702d-9a91-477d-87dd-f610a61773da · outbound

This paper cites Psp-gen: Stochastic inversion of the process–structure–property chain in materials design through deep, generative probabilistic modeling,.

Deep Generative Methods and Tire Architecture Design Psp-gen: Stochastic inversion of the process–structure–property chain in materials design through deep, generative probabilistic modeling,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:09.195136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 352dc228-03c4-4da6-b3e6-27f33300fc8d · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Deep Generative Methods and Tire Architecture Design Score-Based Generative Modeling through Stochastic Differential Equations

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:06.473884Z

Source-reported events for the cited work

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Observation 81bae77c-0281-4b17-9a3a-2f61ff230178 · outbound

This paper cites Diffusion models beat gans on topology optimization,.

Deep Generative Methods and Tire Architecture Design Diffusion models beat gans on topology optimization,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:08.982260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8f0ce8af-e53d-46cf-9f13-1daafd784938 · outbound

This paper cites A data-driven framework for designing microstructure of multifunctional composites with deep- learned diffusion-based generative models,.

Deep Generative Methods and Tire Architecture Design A data-driven framework for designing microstructure of multifunctional composites with deep- learned diffusion-based generative models,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:08.805060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 84b3e579-9a30-4fc6-9af7-9fc3b3420556 · outbound

This paper cites Benchmarking study of deep generative models for inverse polymer design,.

Deep Generative Methods and Tire Architecture Design Benchmarking study of deep generative models for inverse polymer design,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:08.666326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 386f402f-0d57-46f1-871e-d22ef44e8d3b · outbound

This paper cites Generative models struggle with kirigami metamaterials,.

Deep Generative Methods and Tire Architecture Design Generative models struggle with kirigami metamaterials,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:08.494464Z

Source-reported events for the cited work

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Observation 7f96550e-4756-4c56-ac67-a4825223efa3 · outbound

This paper cites Dismai-bench: benchmarking and designing generative models using disordered materials and interfaces,.

Deep Generative Methods and Tire Architecture Design Dismai-bench: benchmarking and designing generative models using disordered materials and interfaces,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:08.371689Z

Source-reported events for the cited work

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Observation d2c281da-7ae1-4d89-8b3e-67c0cb82dce8 · outbound

This paper cites Vibration-based anomaly detection in industrial machines: A comparison of autoencoders and latent spaces,.

Deep Generative Methods and Tire Architecture Design Vibration-based anomaly detection in industrial machines: A comparison of autoencoders and latent spaces,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:08.214044Z

Source-reported events for the cited work

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Observation c2c94318-c58a-4368-80ef-b7d688f5664c · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics,.

Deep Generative Methods and Tire Architecture Design Deep unsupervised learning using nonequilibrium thermodynamics,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:08.092230Z

Source-reported events for the cited work

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Observation e33ea7f0-9112-4a37-a970-5dcaef148442 · outbound

This paper cites Classifier-Free Diffusion Guidance.

Deep Generative Methods and Tire Architecture Design Classifier-Free Diffusion Guidance

Reference 33

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Source-reported events for the cited work

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Observation 076c0fa1-84d3-4d13-b58c-9b9c4a1c8682 · outbound

This paper cites Generalized intersection over union: A metric and a loss for bounding box regression,.

Deep Generative Methods and Tire Architecture Design Generalized intersection over union: A metric and a loss for bounding box regression,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:07.158837Z

Source-reported events for the cited work

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Observation 40497a05-0ba5-4157-af04-e324adb38fe2 · outbound

This paper cites Resampled priors for variational autoencoders,.

Deep Generative Methods and Tire Architecture Design Resampled priors for variational autoencoders,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:07.930429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 803ff81a-5807-43d7-a685-14c5e647758c · outbound

This paper cites beta-vae: Learning basic visual concepts with a constrained variational framework,.

Deep Generative Methods and Tire Architecture Design beta-vae: Learning basic visual concepts with a constrained variational framework,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:07.770651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a8483b02-44e9-4a11-b741-b571545a4042 · outbound

This paper cites Choose k to mirror the encoder depth from Table VIII.

Deep Generative Methods and Tire Architecture Design Choose k to mirror the encoder depth from Table VIII

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:07.637589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

No inbound Pith citation observations are available.