Drivetrain simulation using variational autoencoders | Paper Citation Record

Observation 3d5cb1e3-f4e4-464b-a188-62831e6dfe53 · outbound

This paper cites Advanced estimation techniques for vehicle system dynamic state: A survey.

Drivetrain simulation using variational autoencoders Advanced estimation techniques for vehicle system dynamic state: A survey

Reference 1

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Observation 3322d138-857f-4df6-b4ff-6546d096a5f9 · outbound

This paper cites High performance electric vehicle powertrain modeling, simulation and validation.Energies, 14(5):1493.

Drivetrain simulation using variational autoencoders High performance electric vehicle powertrain modeling, simulation and validation.Energies, 14(5):1493

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:07:38.218827Z

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Drivetrain simulation using variational autoencoders Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-05-23T04:37:33.667060Z

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Observation 67e31c47-2911-4124-a55c-6b17b9091745 · outbound

This paper cites Prediction of electric vehicle driving range and performance characteristics: A review on analytical modeling strategies with its influential factors and improvisation techniques.

Drivetrain simulation using variational autoencoders Prediction of electric vehicle driving range and performance characteristics: A review on analytical modeling strategies with its influential factors and improvisation techniques

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:07:38.167661Z

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Observation faf3073a-3b01-4e68-b52e-f43b231fb924 · outbound

This paper cites InInternational Conference on Machine Learning, pages 10222–10248.

Drivetrain simulation using variational autoencoders InInternational Conference on Machine Learning, pages 10222–10248

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:37:33.659693Z

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Observation 73c8bae5-0e11-4a3f-b398-e9850e35f5f5 · outbound

This paper cites Generative learning for nonlinear dynamics.Nature Reviews Physics, 6(3):194–206.

Drivetrain simulation using variational autoencoders Generative learning for nonlinear dynamics.Nature Reviews Physics, 6(3):194–206

Reference 6

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This paper cites RAVE: A variational autoencoder for fast and high-quality neural audio synthesis.

Drivetrain simulation using variational autoencoders RAVE: A variational autoencoder for fast and high-quality neural audio synthesis

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-23T04:32:33.398716Z

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Observation b72ec8df-0b19-45bd-ba9d-5889f1d2ebc8 · outbound

This paper cites Asemi-supervisedautoencoderframeworkforjointgeneration and classification of breathing.Computer Methods and Programs in Biomedicine, 209:106312.

Drivetrain simulation using variational autoencoders Asemi-supervisedautoencoderframeworkforjointgeneration and classification of breathing.Computer Methods and Programs in Biomedicine, 209:106312

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:37:33.651653Z

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Observation 4fe69918-c75b-43ca-b53a-f8f5b0fc0884 · outbound

This paper cites Auto-Encoding Variational Bayes.

Drivetrain simulation using variational autoencoders Auto-Encoding Variational Bayes

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-23T04:32:33.391495Z

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Observation c941e43b-dc71-4a1c-8106-7f01354334f8 · outbound

This paper cites Conditional variational autoencoder for learned image reconstruction.

Drivetrain simulation using variational autoencoders Conditional variational autoencoder for learned image reconstruction

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:37:33.655393Z

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Observation 55170458-a0cc-4cc9-90f0-d64769f0f786 · outbound

This paper cites Variationalautoencoderwithoptimizing Gaussian mixture model priors.IEEE Access, 8:43992–44005.

Drivetrain simulation using variational autoencoders Variationalautoencoderwithoptimizing Gaussian mixture model priors.IEEE Access, 8:43992–44005

Reference 11

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Observation 05f3e466-6ada-4eec-bb05-f3547421b43d · outbound

This paper cites Machine learning of Kondo physics using variational autoencoders and symbolic regression.Physical Review B, 104(23): 235111.

Drivetrain simulation using variational autoencoders Machine learning of Kondo physics using variational autoencoders and symbolic regression.Physical Review B, 104(23): 235111

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:37:33.620001Z

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Observation 5b8c71d3-4df7-4939-8bbf-7ede624cdfe8 · outbound

This paper cites Latent space interpretation and visualisation for understanding the decisions of con- volutional variational autoencoders trained with EEG topographic maps.

Drivetrain simulation using variational autoencoders Latent space interpretation and visualisation for understanding the decisions of con- volutional variational autoencoders trained with EEG topographic maps

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:37:33.616367Z

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Observation 127c48bb-e1d7-4da4-9d18-2a9177e7a17f · outbound

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Drivetrain simulation using variational autoencoders Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-05-23T04:37:33.622906Z

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Observation d3c6823d-bcad-4111-a3c8-654510fa385e · outbound

This paper cites Drivability optimization by reducing oscillation of electric vehicle drivetrains.World Electric Vehicle Journal, 11(4):68.

Drivetrain simulation using variational autoencoders Drivability optimization by reducing oscillation of electric vehicle drivetrains.World Electric Vehicle Journal, 11(4):68

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:37:33.633661Z

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Observation da53791f-1f0f-4831-bd00-8576d9a94471 · outbound

This paper cites Anti-jerk controllers for automotive applications: A review.Annual Reviews in Control, 50:174–189.

Drivetrain simulation using variational autoencoders Anti-jerk controllers for automotive applications: A review.Annual Reviews in Control, 50:174–189

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:37:33.606126Z

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Observation 60d6a817-8ec3-4966-af9b-d1522a90d466 · outbound

This paper cites Comparison of anti-jerk controllers for electric vehicles with on-board motors.IEEE Transactions on Vehicular Technology, 69(10):10681–10699.

Drivetrain simulation using variational autoencoders Comparison of anti-jerk controllers for electric vehicles with on-board motors.IEEE Transactions on Vehicular Technology, 69(10):10681–10699

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:37:33.591069Z

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Observation f120abd3-8e9c-4c83-9832-637ee4deb6e3 · outbound

This paper cites Methods for virtual validation of automotive powertrain systems in terms of vehicle drivability—a systematic literature review.IEEE Access, 11:27043–27065.

Drivetrain simulation using variational autoencoders Methods for virtual validation of automotive powertrain systems in terms of vehicle drivability—a systematic literature review.IEEE Access, 11:27043–27065

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:37:33.598661Z

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Observation 8a1699fc-e875-47c1-a545-202820466694 · outbound

This paper cites Torsional finite elements and nonlinear numerical modelling in vehicle powertrain dynamics.

Drivetrain simulation using variational autoencoders Torsional finite elements and nonlinear numerical modelling in vehicle powertrain dynamics

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:37:33.609728Z

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Observation bdd68c82-e802-40d9-a3eb-15b0f294aef9 · outbound

This paper cites Jerk analysisofapower-splithybridelectricvehiclebasedonadata-drivenvehicledynamicsmodel.

Drivetrain simulation using variational autoencoders Jerk analysisofapower-splithybridelectricvehiclebasedonadata-drivenvehicledynamicsmodel

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:37:33.637141Z

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Observation 22947972-e7c3-4612-b39d-a15ba6177351 · outbound

This paper cites Data-driven vehicle modeling of longitudinal dynamics based on a multibody model and deep neural networks.Measurement, 180:109541.

Drivetrain simulation using variational autoencoders Data-driven vehicle modeling of longitudinal dynamics based on a multibody model and deep neural networks.Measurement, 180:109541

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:37:33.671005Z

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Observation f0345dc8-1ebf-423f-a5d7-de716df10351 · outbound

This paper cites Longitudinal vehicle dynamics: A comparison of physical and data-driven models under large-scale real-world driving conditions.IEEE Access, 8:73714–73729.

Drivetrain simulation using variational autoencoders Longitudinal vehicle dynamics: A comparison of physical and data-driven models under large-scale real-world driving conditions.IEEE Access, 8:73714–73729

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:07:38.164418Z

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Observation 3e2ac3bf-bb62-492b-a75d-2a7a3528a372 · outbound

This paper cites and Lal Priya P.S.

Drivetrain simulation using variational autoencoders and Lal Priya P.S

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:07:38.228091Z

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Observation 99175f0b-6f83-4b0d-90e7-921a1bcd3359 · outbound

This paper cites Transient system model for the analysis of structural dynamic interactions of electric drivetrains.Energies, 14(4).

Drivetrain simulation using variational autoencoders Transient system model for the analysis of structural dynamic interactions of electric drivetrains.Energies, 14(4)

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:07:38.215750Z

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Observation 8a7d137f-bfa8-4ae1-b27d-7699b5803809 · outbound

This paper cites Integrated design of the torsion beam electric driving axle.Journal of Vibroengineering, pages 537–549, jan 2022.

Drivetrain simulation using variational autoencoders Integrated design of the torsion beam electric driving axle.Journal of Vibroengineering, pages 537–549, jan 2022

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:07:38.180016Z

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Observation b9d8efad-3cc1-46a2-b59c-981d8ec909dd · outbound

This paper cites Modeling and simulation of the complete electric power train of a hybrid electric vehicle.

Drivetrain simulation using variational autoencoders Modeling and simulation of the complete electric power train of a hybrid electric vehicle

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:07:38.158402Z

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Observation 76ae2935-9ef6-4c9a-a816-0f3a8bd125b3 · outbound

This paper cites McDonald.

Drivetrain simulation using variational autoencoders McDonald

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:07:38.197431Z

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Observation 683f1058-8deb-4436-bd1b-c3981b9567de · outbound

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Drivetrain simulation using variational autoencoders Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-05-23T06:07:38.188528Z

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Observation e0dc7f45-10e5-4f52-8e5e-71bbaad720f0 · outbound

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Drivetrain simulation using variational autoencoders Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-05-23T06:07:38.191340Z

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Observation 3ed65e63-096d-47ca-8dbb-43322588dd1c · outbound

This paper cites In2022 4th International Conference on Frontiers Technology of Information and Computer (ICFTIC), pages 511–514.

Drivetrain simulation using variational autoencoders In2022 4th International Conference on Frontiers Technology of Information and Computer (ICFTIC), pages 511–514

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:07:38.155116Z

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Observation fcadbf72-47f5-421c-8e40-1382bd5438ae · outbound

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Drivetrain simulation using variational autoencoders Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-05-23T06:07:38.161115Z

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Observation 2e338b4e-bdbd-446d-a798-c929f026a241 · outbound

This paper cites Data-Augmented Predictive Deep Neural Network: Enhancing the extrapolation capabilities of non-intrusive surrogate models.

Drivetrain simulation using variational autoencoders Data-Augmented Predictive Deep Neural Network: Enhancing the extrapolation capabilities of non-intrusive surrogate models

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Resolution: verified exact; arxiv_id, observed 2026-07-27T01:19:44.612868Z

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Observation 7757f70b-1056-4988-b6a6-db1f2b2d936f · outbound

This paper cites Raissi, P.

Drivetrain simulation using variational autoencoders Raissi, P

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:07:38.182763Z

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Observation 13ffeaa9-49a8-4c22-b3d3-3a239b475111 · outbound

This paper cites Learning nonlinear operators in latentspacesforreal-timepredictionsofcomplexdynamicsinphysicalsystems.

Drivetrain simulation using variational autoencoders Learning nonlinear operators in latentspacesforreal-timepredictionsofcomplexdynamicsinphysicalsystems

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:07:38.206633Z

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Observation 61f6b9b7-aaee-402b-ab8b-53bbb4c95474 · outbound

This paper cites State-space models are accurate and efficient neural operators for dynamical systems.

Drivetrain simulation using variational autoencoders State-space models are accurate and efficient neural operators for dynamical systems

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-23T04:32:33.384870Z

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Observation 72be92f9-a70e-40ca-b986-ffea2a119840 · outbound

This paper cites Hybrid physics-based and data-driven models for smart manufacturing: Modelling, simulation, and explainability.Journal of Manufacturing Systems, 63:381–391.

Drivetrain simulation using variational autoencoders Hybrid physics-based and data-driven models for smart manufacturing: Modelling, simulation, and explainability.Journal of Manufacturing Systems, 63:381–391

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:07:38.203727Z

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Observation 84a1d283-179b-49f9-8bdb-6ab6db266c83 · outbound

This paper cites Physics- basedanddata-drivenhybridmodelinginmanufacturing:areview.

Drivetrain simulation using variational autoencoders Physics- basedanddata-drivenhybridmodelinginmanufacturing:areview

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:07:38.174026Z

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Observation 483fd2de-ec8d-4646-83af-0481e1c88c71 · outbound

This paper cites Hybrid modeling of first-principles and machine learning: A step-by-step tutorial review for practical implementation.Computers & Chemical Engineering, page 108926.

Drivetrain simulation using variational autoencoders Hybrid modeling of first-principles and machine learning: A step-by-step tutorial review for practical implementation.Computers & Chemical Engineering, page 108926

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:07:38.234534Z

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Observation ea557ee6-5797-46e4-9f7b-3e6dbce6d4b8 · outbound

This paper cites AI-basedmethodologyforthecalibrationofanti-jerkcontrolonelectricdrives.

Drivetrain simulation using variational autoencoders AI-basedmethodologyforthecalibrationofanti-jerkcontrolonelectricdrives

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:07:38.238048Z

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Observation 830b5fef-e08f-4e0d-826d-b842cbc12d19 · outbound

This paper cites Griffin and J.

Drivetrain simulation using variational autoencoders Griffin and J

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:07:38.152148Z

Source-reported events for the cited work

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Observation 74e500ac-19ba-463e-8424-2171e5f889fb · outbound

This paper cites Pearson Education India.

Drivetrain simulation using variational autoencoders Pearson Education India

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:07:38.171002Z

Source-reported events for the cited work

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Observation 9b2eb4fa-48bb-46bb-954a-7635295581d0 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.Advances in neural information processing systems, 32.

Drivetrain simulation using variational autoencoders Pytorch: An imperative style, high-performance deep learning library.Advances in neural information processing systems, 32

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:07:38.222596Z

Source-reported events for the cited work

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Observation 5c6b939c-e38a-48e5-8837-93b99ef468ed · outbound

This paper cites A comprehensive survey and analysis of generative models in machine learning.Computer Science Review, 38:100285.

Drivetrain simulation using variational autoencoders A comprehensive survey and analysis of generative models in machine learning.Computer Science Review, 38:100285

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:07:38.241150Z

Source-reported events for the cited work

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Observation 6f295b04-6d68-4d68-97ec-535345e2a8db · outbound

This paper cites Tutorial on Variational Autoencoders.

Drivetrain simulation using variational autoencoders Tutorial on Variational Autoencoders

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Resolution: verified exact; arxiv_id, observed 2026-05-23T04:32:33.369239Z

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Observation d4df61a1-0051-483e-8fac-920dd7a69019 · outbound

This paper cites Kingma and Max Welling.

Drivetrain simulation using variational autoencoders Kingma and Max Welling

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:07:38.231257Z

Source-reported events for the cited work

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Observation 5d70d348-bff5-49db-aede-128dae5d28d8 · outbound

This paper cites an unresolved cited work.

Drivetrain simulation using variational autoencoders Unresolved cited work

Reference 46

Resolution: unresolved; raw_fallback, observed 2026-05-23T06:07:38.194483Z

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Observation 9e9811c4-c97a-4562-a6db-23011192f6e8 · outbound

This paper cites Kokoszka and G.

Drivetrain simulation using variational autoencoders Kokoszka and G

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:07:38.185912Z

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Observation 4213b235-6809-420a-9884-e8cb18642c20 · outbound

This paper cites Statsmodels: econometric and statistical modeling with python.SciPy, 7(1).

Drivetrain simulation using variational autoencoders Statsmodels: econometric and statistical modeling with python.SciPy, 7(1)

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:07:38.213075Z

Source-reported events for the cited work

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Observation ae99f80f-87c0-43aa-840d-3ec25baab408 · outbound

This paper cites Theoretical foundations of t-SNE for visualizing high-dimensional clustered data.Journal of Machine Learning Research, 23(301):1–54.

Drivetrain simulation using variational autoencoders Theoretical foundations of t-SNE for visualizing high-dimensional clustered data.Journal of Machine Learning Research, 23(301):1–54

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:07:38.200673Z

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Observation 51da9432-9d23-48c5-b38f-55115160d06e · outbound

This paper cites Visualizing data using t-SNE.Journal of machine learning research, 9(11).

Drivetrain simulation using variational autoencoders Visualizing data using t-SNE.Journal of machine learning research, 9(11)

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:37:33.663574Z

Source-reported events for the cited work

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Observation e7d731c7-b81e-4f11-af91-e345eb223007 · outbound

This paper cites Neighbourhood component analysis.Adv.

Drivetrain simulation using variational autoencoders Neighbourhood component analysis.Adv

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:07:38.225357Z

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Observation fd23c2ec-1bf1-413b-83c7-b06c372e82c1 · outbound

This paper cites Vanderplas, A.

Drivetrain simulation using variational autoencoders Vanderplas, A

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:37:33.644378Z

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Observation 42b78122-ae85-43f3-9366-35d8f3b426f3 · outbound

This paper cites Modelling framework for artificial hybrid dynamical systems.

Drivetrain simulation using variational autoencoders Modelling framework for artificial hybrid dynamical systems

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:37:33.647888Z

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Observation f8e00ef1-f179-4347-9c2b-b1724675d1f0 · outbound

This paper cites Jiang, Senwei Liang, and Haizhao Yang.

Drivetrain simulation using variational autoencoders Jiang, Senwei Liang, and Haizhao Yang

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T06:07:38.177059Z

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Observation 30ae0319-8bad-4491-8064-c6c9894480b2 · outbound

This paper cites Lindemann, D.

Drivetrain simulation using variational autoencoders Lindemann, D

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:37:33.626386Z

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Observation 436de4f3-df69-4d35-a34a-f481c85be844 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

Drivetrain simulation using variational autoencoders U-net: Convolutional networks for biomedical image segmentation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:37:33.629987Z

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Observation e4ec0176-cf78-491a-ba6c-7d66162f9dad · outbound

This paper cites Wave-U-Net: A multi-scale neural network for end-to-end audio source separation.

Drivetrain simulation using variational autoencoders Wave-U-Net: A multi-scale neural network for end-to-end audio source separation

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:37:33.612915Z

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Observation c228f60e-939e-4430-a93c-cacbe60cdc75 · outbound

This paper cites Aconvnetforthe2020s.

Drivetrain simulation using variational autoencoders Aconvnetforthe2020s

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:37:33.602674Z

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Observation 25966507-07f8-48e4-8cc8-30c6913e4b39 · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

Drivetrain simulation using variational autoencoders Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:37:33.594870Z

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