Variational Rank Reduction Autoencoders for Generative Thermal Design | Paper Citation Record

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This paper cites Real-Time 2D Temperature Field Prediction in Metal Additive Manufacturing Using Physics-Informed Neural Networks.

Variational Rank Reduction Autoencoders for Generative Thermal Design Real-Time 2D Temperature Field Prediction in Metal Additive Manufacturing Using Physics-Informed Neural Networks

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This paper cites A study of simulation of the urban space 3d temperature field at a community scale based on high-resolution remote sensing and cfd.Remote Sensing, 14(13):3174, 2022.

Variational Rank Reduction Autoencoders for Generative Thermal Design A study of simulation of the urban space 3d temperature field at a community scale based on high-resolution remote sensing and cfd.Remote Sensing, 14(13):3174, 2022

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This paper cites Estimating urban spatial temperatures considering anthropogenic heat release factors focusing on the mobility characteristics.Sustainable Cities and Society, 85:104073, 2022.

Variational Rank Reduction Autoencoders for Generative Thermal Design Estimating urban spatial temperatures considering anthropogenic heat release factors focusing on the mobility characteristics.Sustainable Cities and Society, 85:104073, 2022

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This paper cites Machine learning for urban heat island (uhi) analysis: Predicting land surface temperature (lst) in urban environments.Urban Climate, 55:101962, 2024.

Variational Rank Reduction Autoencoders for Generative Thermal Design Machine learning for urban heat island (uhi) analysis: Predicting land surface temperature (lst) in urban environments.Urban Climate, 55:101962, 2024

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Variational Rank Reduction Autoencoders for Generative Thermal Design 3-d fem analysis of the temperature field and the thermal stress for plastics thermalforming.Journal of Materials Processing Technology, 97(1-3):35–43, 2000

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This paper cites Chaquet and Pedro Galán del Sastre.

Variational Rank Reduction Autoencoders for Generative Thermal Design Chaquet and Pedro Galán del Sastre

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This paper cites Evolutionary opti- mization methods for high-dimensional expensive problems: A survey.IEEE/CAA Journal of Automatica Sinica, 11(5):1092–1105, 2024.

Variational Rank Reduction Autoencoders for Generative Thermal Design Evolutionary opti- mization methods for high-dimensional expensive problems: A survey.IEEE/CAA Journal of Automatica Sinica, 11(5):1092–1105, 2024

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Observation 1cf042f0-af3b-4b8b-8519-82f9376feb04 · outbound

This paper cites Gary Wang.

Variational Rank Reduction Autoencoders for Generative Thermal Design Gary Wang

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Observation c1cc48c7-f57f-4325-b47e-2c675daaeda9 · outbound

This paper cites A study on improving temperature field prediction accuracy using a surrogate model assisted by airflow field.Results in Engineering, 25:104544, 2025.

Variational Rank Reduction Autoencoders for Generative Thermal Design A study on improving temperature field prediction accuracy using a surrogate model assisted by airflow field.Results in Engineering, 25:104544, 2025

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Observation e9d066d2-20d8-44f8-b8e5-665f37345f03 · outbound

This paper cites A physics- informed machine learning approach for temperature field prediction in metallic additive manufacturing.Journal of Industrial Information Integration, page 100899, 2025.

Variational Rank Reduction Autoencoders for Generative Thermal Design A physics- informed machine learning approach for temperature field prediction in metallic additive manufacturing.Journal of Industrial Information Integration, page 100899, 2025

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This paper cites Inferring turbulent velocity and temperature fields and their statistics from Lagrangian velocity measurements using physics-informed Kolmogorov-Arnold Networks.

Variational Rank Reduction Autoencoders for Generative Thermal Design Inferring turbulent velocity and temperature fields and their statistics from Lagrangian velocity measurements using physics-informed Kolmogorov-Arnold Networks

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This paper cites Learning nonlinear operators via deeponet based on the universal approximation theorem of operators.Nature Machine Intelligence, 3(3):218–229, March 2021.

Variational Rank Reduction Autoencoders for Generative Thermal Design Learning nonlinear operators via deeponet based on the universal approximation theorem of operators.Nature Machine Intelligence, 3(3):218–229, March 2021

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This paper cites Physically interpretable airfoil parameterization using variational autoencoder-based generative modeling.

Variational Rank Reduction Autoencoders for Generative Thermal Design Physically interpretable airfoil parameterization using variational autoencoder-based generative modeling

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Observation af368209-acf4-473b-b480-374da9b1e6c2 · outbound

This paper cites A generative design method of airfoil based on conditional variational autoencoder.Engineering Applications of Artificial Intelligence, 139:109461, 2025.

Variational Rank Reduction Autoencoders for Generative Thermal Design A generative design method of airfoil based on conditional variational autoencoder.Engineering Applications of Artificial Intelligence, 139:109461, 2025

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This paper cites Using a generative adversarial network for the inverse design of soft morphing composite beams.Engineering Applications of Artificial Intelligence, 133:108527, 2024.

Variational Rank Reduction Autoencoders for Generative Thermal Design Using a generative adversarial network for the inverse design of soft morphing composite beams.Engineering Applications of Artificial Intelligence, 133:108527, 2024

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Variational Rank Reduction Autoencoders for Generative Thermal Design Representation learning: A review and new perspectives

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Variational Rank Reduction Autoencoders for Generative Thermal Design Rank reduction autoencoders, 2025

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Variational Rank Reduction Autoencoders for Generative Thermal Design Variational rank reduction autoencoder.arXiv preprint arXiv:2505.09458, 2025

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Variational Rank Reduction Autoencoders for Generative Thermal Design Deep generative models in engineering design: A review.Journal of Mechanical Design, 144(7):071704, 2022

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Variational Rank Reduction Autoencoders for Generative Thermal Design Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections

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Variational Rank Reduction Autoencoders for Generative Thermal Design Auto-encoding variational bayes, 2013

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Variational Rank Reduction Autoencoders for Generative Thermal Design An indirect design representation for topology optimization using variational autoencoder and style transfer

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Variational Rank Reduction Autoencoders for Generative Thermal Design Research on multi-heat source arrangement optimization based on equivalent heat source method and reconstructed variational autoencoder.Scientific Reports, 14(1):21208, 2024

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Variational Rank Reduction Autoencoders for Generative Thermal Design Gaussian process prior variational autoencoders.Advances in neural information processing systems, 31, 2018

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Variational Rank Reduction Autoencoders for Generative Thermal Design Generative adversarial nets.Advances in neural information processing systems, 27, 2014

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Variational Rank Reduction Autoencoders for Generative Thermal Design An adaptive artificial neural network-based generative design method for layout designs.International Journal of Heat and Mass Transfer, 184:122313, 2022

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Variational Rank Reduction Autoencoders for Generative Thermal Design A continuous genetic algorithm designed for the global optimization of multimodal functions.Journal of Heuristics, 6(2):191–213, 2000

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Variational Rank Reduction Autoencoders for Generative Thermal Design Thermodynamics- informed super-resolution of scarce temporal dynamics data.Computer Methods in Applied Mechanics and Engineering, 430:117210, 2024

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Variational Rank Reduction Autoencoders for Generative Thermal Design Physics-integrated variational autoencoders for robust and interpretable generative modeling.Advances in Neural Information Processing Systems, 34:14809–14821, 2021

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Variational Rank Reduction Autoencoders for Generative Thermal Design Symplectic encoders for physics-constrained variational dynamics inference.Scientific Reports, 13(1):2643, 2023

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Variational Rank Reduction Autoencoders for Generative Thermal Design Pi-vae: Physics-informed variational auto-encoder for stochastic differential equations.Computer Methods in Applied Mechanics and Engineering, 403:115664, 2023

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Variational Rank Reduction Autoencoders for Generative Thermal Design Generating required motor rotor shape by physics-guided vae/wgan-gp.Results in Engineering, page 106181, 2025

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Variational Rank Reduction Autoencoders for Generative Thermal Design Topological autoencoders

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Variational Rank Reduction Autoencoders for Generative Thermal Design Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flows.Expert Systems with Applications, 202:117038, 2022

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Variational Rank Reduction Autoencoders for Generative Thermal Design A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized pdes.Journal of Scientific Computing, 87(2):61, 2021

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Variational Rank Reduction Autoencoders for Generative Thermal Design A graph convolutional autoencoder approach to model order reduction for parametrized pdes.Journal of Computational Physics, 501:112762, 2024

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Variational Rank Reduction Autoencoders for Generative Thermal Design Latent neural operator for solving forward and inverse pde problems.Advances in Neural Information Processing Systems, 37:33085–33107, 2024

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Variational Rank Reduction Autoencoders for Generative Thermal Design Investigation and implementation of model order reduction technique for large scale dynamical systems.Archives of Computational Methods in Engineering, 29(5):3087– 3108, 2022

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Variational Rank Reduction Autoencoders for Generative Thermal Design Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders.Physics of Fluids, 33(3), 2021

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Variational Rank Reduction Autoencoders for Generative Thermal Design Discovering governing equations from partial measurements with deep delay autoencoders.Proceedings of the Royal Society A, 479(2276):20230422, 2023

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Variational Rank Reduction Autoencoders for Generative Thermal Design Thermodynamically Consistent Latent Dynamics Identification for Parametric Systems

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Variational Rank Reduction Autoencoders for Generative Thermal Design Physics-informed geometry-aware neural operator.Computer Methods in Applied Mechanics and Engineering, 434:117540, 2025

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Variational Rank Reduction Autoencoders for Generative Thermal Design Deep learning of thermodynamics-aware reduced-order models from data.Computer Methods in Applied Mechanics and Engineering, 379:113763, 2021

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Variational Rank Reduction Autoencoders for Generative Thermal Design Physics perception in sloshing scenes with guaranteed thermodynamic consistency.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(2):2136–2150, 2022

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Variational Rank Reduction Autoencoders for Generative Thermal Design Physics-informed neural ode (pinode): embedding physics into models using collocation points.Scientific Reports, 13(1):10166, 2023

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Resolution: unresolved; no resolver link, observed 2026-08-04T20:35:23.555426Z

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Observation 3bf85bff-0d0b-42de-81ba-9038824bdabf · outbound

This paper cites Latentpinns: Generative physics-informed neural networks via a latent representation learning.Artificial Intelligence in Geosciences, page 100115, 2025.

Variational Rank Reduction Autoencoders for Generative Thermal Design Latentpinns: Generative physics-informed neural networks via a latent representation learning.Artificial Intelligence in Geosciences, page 100115, 2025

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Resolution: unresolved; no resolver link, observed 2026-08-04T20:35:23.558142Z

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Observation 915c1403-4fe6-49cc-95f7-1f4f03f885c0 · outbound

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

Variational Rank Reduction Autoencoders for Generative Thermal Design Learning two-phase microstructure evolution using neural operators and autoencoder architectures.npj Computational Materials, 8(1):190, 2022

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-04T20:35:23.560892Z

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Observation 6b021c54-d322-46dd-8d05-17fca32ac073 · outbound

This paper cites Physics-enhanced machine learning: a position paper for dynamical systems investigations.

Variational Rank Reduction Autoencoders for Generative Thermal Design Physics-enhanced machine learning: a position paper for dynamical systems investigations

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-04T20:35:23.563557Z

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Observation 74fbe9d6-dad1-4fd5-8f05-25f18e1ccbce · outbound

This paper cites Physics-guided deep markov models for learning nonlinear dynamical systems with uncertainty.Mechanical Systems and Signal Processing, 178:109276, 2022.

Variational Rank Reduction Autoencoders for Generative Thermal Design Physics-guided deep markov models for learning nonlinear dynamical systems with uncertainty.Mechanical Systems and Signal Processing, 178:109276, 2022

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Resolution: unresolved; no resolver link, observed 2026-08-04T20:35:23.566216Z

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Observation 48f4054a-4f0d-4d9d-add3-3a13edb6d137 · outbound

This paper cites Extracting interpretable physical parameters from spatiotemporal systems using unsupervised learning.Physical Review X, 10(3):031056, 2020.

Variational Rank Reduction Autoencoders for Generative Thermal Design Extracting interpretable physical parameters from spatiotemporal systems using unsupervised learning.Physical Review X, 10(3):031056, 2020

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-04T20:35:23.568897Z

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Observation 10b5460f-2460-4cbb-8063-6ccb077e9710 · outbound

This paper cites Solving inverse-pde problems with physics-aware neural networks.Journal of Computational Physics, 440:110414, 2021.

Variational Rank Reduction Autoencoders for Generative Thermal Design Solving inverse-pde problems with physics-aware neural networks.Journal of Computational Physics, 440:110414, 2021

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-04T20:35:23.571651Z

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Observation 7549aacc-0893-46c7-8605-d9d8c2c0f052 · outbound

This paper cites Discovering sparse interpretable dynamics from partial observations.Communications Physics, 5(1):206, 2022.

Variational Rank Reduction Autoencoders for Generative Thermal Design Discovering sparse interpretable dynamics from partial observations.Communications Physics, 5(1):206, 2022

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-04T20:35:23.574447Z

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Observation 0f974b5e-2567-4041-be4b-c76f4fb2bd8b · outbound

This paper cites Learning proper orthogonal decomposition of complex dynamics using heavy-ball neural odes.Journal of Scientific Computing, 95(2):54, 2023.

Variational Rank Reduction Autoencoders for Generative Thermal Design Learning proper orthogonal decomposition of complex dynamics using heavy-ball neural odes.Journal of Scientific Computing, 95(2):54, 2023

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-04T20:35:23.577170Z

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Observation 06b63a30-a316-4fb0-bd47-cde1c2543902 · outbound

This paper cites Reduced basis approximations of parameterized dynamical partial differential equations via neural networks.Foundations of Data Science, 7(SAND-2025-04099J), 2025.

Variational Rank Reduction Autoencoders for Generative Thermal Design Reduced basis approximations of parameterized dynamical partial differential equations via neural networks.Foundations of Data Science, 7(SAND-2025-04099J), 2025

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-04T20:35:23.579779Z

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Observation 8d1bd8f0-5e6a-426c-b0a8-49f33c22c77d · outbound

This paper cites Nonlinear model reduction for operator learning.

Variational Rank Reduction Autoencoders for Generative Thermal Design Nonlinear model reduction for operator learning

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-04T20:35:23.582414Z

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