PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations | Paper Citation Record

Observation d04ea927-2519-4c26-82d6-2951fe1bd198 · outbound

This paper cites Photo-realistic single image super-resolution using a generative adversarial network,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Photo-realistic single image super-resolution using a generative adversarial network,

Reference 1

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This paper cites Image super- resolution: A comprehensive review, recent trends, challenges and applications,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Image super- resolution: A comprehensive review, recent trends, challenges and applications,

Reference 2

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This paper cites Physics guided neural networks for spatio-temporal super-resolution of turbulent flows,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Physics guided neural networks for spatio-temporal super-resolution of turbulent flows,

Reference 3

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Observation f13e0538-1371-420c-827b-d7448c0b42f7 · outbound

This paper cites TempoGAN: A tempo- rally coherent, volumetric GAN for super-resolution fluid flow,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations TempoGAN: A tempo- rally coherent, volumetric GAN for super-resolution fluid flow,

Reference 4

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This paper cites Improved Denoising Diffusion Proba- bilistic Models,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Improved Denoising Diffusion Proba- bilistic Models,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:18.914666Z

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Observation 8e0aaa0d-fc7d-489e-ac74-6f529d25f31d · outbound

This paper cites Generative adver- sarial nets,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Generative adver- sarial nets,

Reference 6

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Observation 5b0c376b-0e9e-418b-b4b3-332dec65293f · outbound

This paper cites Using physics-informed enhanced super-resolution generative adversarial networks for subfilter modeling in turbulent reactive flows,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Using physics-informed enhanced super-resolution generative adversarial networks for subfilter modeling in turbulent reactive flows,

Reference 7

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Observation dc10865b-23cb-4c36-9b63-68966cfc6656 · outbound

This paper cites Grigis and J.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Grigis and J

Reference 8

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Observation 12afd457-6c17-44cb-a675-754c33121469 · outbound

This paper cites Incorporating physics into data-driven computer vision,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Incorporating physics into data-driven computer vision,

Reference 9

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Observation 3f48a913-c9b4-4da2-8130-5ea9cb44722d · outbound

This paper cites Esrgan: Enhanced super-resolution generative adversarial networks,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Esrgan: Enhanced super-resolution generative adversarial networks,

Reference 10

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This paper cites an unresolved cited work.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Unresolved cited work

Reference 11

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Observation a003247e-7c4e-4653-aa36-ee8c275e0e2a · outbound

This paper cites Very deep convolutional networks for large-scale image recognition,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Very deep convolutional networks for large-scale image recognition,

Reference 12

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Observation 1341e750-1d5d-4630-98c7-e127202aa73f · outbound

This paper cites Physics-informed neural networks for heat transfer problems,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Physics-informed neural networks for heat transfer problems,

Reference 13

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Observation f66f4c63-6269-4524-9468-9d1eb818c6a4 · outbound

This paper cites Physics- informed neural networks (pinns) for fluid mechanics: A review,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Physics- informed neural networks (pinns) for fluid mechanics: A review,

Reference 14

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

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,

Reference 15

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This paper cites SuperBench: A Super-Resolution Benchmark Dataset for Scientific Machine Learning.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations SuperBench: A Super-Resolution Benchmark Dataset for Scientific Machine Learning

Reference 16

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This paper cites Image quality assessment through FSIM, SSIM, MSE and PSNR—A comparative study,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Image quality assessment through FSIM, SSIM, MSE and PSNR—A comparative study,

Reference 17

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Observation c4e976e6-1f97-4ccf-8c11-a7d225bb1c9a · outbound

This paper cites Image quality assessment: From error visibility to structural similarity,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Image quality assessment: From error visibility to structural similarity,

Reference 18

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Observation f058b01e-fb1f-4920-9b47-b30d68d9e079 · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations The unreasonable effectiveness of deep features as a perceptual metric,

Reference 19

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Observation fd003196-dd61-463c-8497-e102b771faf1 · outbound

This paper cites Deep learning-enabled resolution-enhancement in mini- and regular mi- croscopy for biomedical imaging,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Deep learning-enabled resolution-enhancement in mini- and regular mi- croscopy for biomedical imaging,

Reference 20

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Observation 11d93e1b-ccbd-4030-b0e3-b79c1bf0776c · outbound

This paper cites Deepthi, A.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Deepthi, A

Reference 21

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Observation 43021c0a-a23b-4097-9f5a-dba4dd9725d7 · outbound

This paper cites PDEBench: An Exten- sive Benchmark for Scientific Machine Learning,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations PDEBench: An Exten- sive Benchmark for Scientific Machine Learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:18.844545Z

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Observation 74a538d2-b2d2-4e28-bee7-3991752918ca · outbound

This paper cites Shalev-Shwartz and S.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Shalev-Shwartz and S

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:18.834735Z

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Observation 8bdbf031-5a24-460d-9125-675c1df97f21 · outbound

This paper cites A variational splitting of high-order linear multistep methods for heat transfer and advection–diffusion parabolic problems,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations A variational splitting of high-order linear multistep methods for heat transfer and advection–diffusion parabolic problems,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:18.825298Z

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Observation 071cc353-f422-417a-ae1b-6edf6d085dcb · outbound

This paper cites Estimates on the generalization error of physics-informed neural networks for approximating PDEs,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Estimates on the generalization error of physics-informed neural networks for approximating PDEs,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:18.815635Z

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Observation 5eb57a53-db6e-455f-847b-f2cccc04dfdc · outbound

This paper cites Physics and equality constrained artificial neural networks: Application to forward and inverse problems with multi-fidelity data fusion,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Physics and equality constrained artificial neural networks: Application to forward and inverse problems with multi-fidelity data fusion,

Reference 26

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Observation 5c0e5e93-7f1c-4aec-8bf9-d518255a2171 · outbound

This paper cites Feature-adjacent multi-fidelity physics- informed machine learning for partial differential equations,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Feature-adjacent multi-fidelity physics- informed machine learning for partial differential equations,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:18.806203Z

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Observation bdab70b7-8d05-4146-99ae-8c3c6e9ce392 · outbound

This paper cites Message Passing Neural PDE Solvers.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Message Passing Neural PDE Solvers

Reference 28

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Observation f4e047d2-2294-4a6c-a689-2a90a12142a1 · outbound

This paper cites Neural operators for accelerating scientific simulations and design,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Neural operators for accelerating scientific simulations and design,

Reference 29

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Observation 871ac6e5-af3f-4887-a779-e3a6f9adb8c4 · outbound

This paper cites Differentiable modelling to unify machine learning and physical models for geosciences,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Differentiable modelling to unify machine learning and physical models for geosciences,

Reference 30

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Observation d3cffb5d-1fa6-493f-9c77-dd68b54bef8f · outbound

This paper cites Analytical, semi- analytical, and numerical solutions for the cahn–allen equation,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Analytical, semi- analytical, and numerical solutions for the cahn–allen equation,

Reference 31

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Observation cad997db-863f-4c07-a288-6a2d62fdd35e · outbound

This paper cites Phase-field models for microstructure evolution,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Phase-field models for microstructure evolution,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:18.796426Z

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Observation 87bf03cc-126a-4895-9ee2-65bc358c8411 · outbound

This paper cites Geometrical image segmen- tation by the allen–cahn equation,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Geometrical image segmen- tation by the allen–cahn equation,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:18.786893Z

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Observation a0ca5306-bbaa-478b-8889-0a6ff4f030d4 · outbound

This paper cites Analytical and numerical solutions of mathematical biology models: The newell- whitehead-segel and allen-cahn equations,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Analytical and numerical solutions of mathematical biology models: The newell- whitehead-segel and allen-cahn equations,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:18.777027Z

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This paper cites Localized folding of thick layers,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Localized folding of thick layers,

Reference 35

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Observation 96c92554-33e7-4bf3-8375-c935b83e237d · outbound

This paper cites An unconditionally stable hybrid numerical method for solving the Allen–Cahn equation,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations An unconditionally stable hybrid numerical method for solving the Allen–Cahn equation,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:18.757378Z

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Observation 75925433-ec10-4d68-82e6-45c3d47716d8 · outbound

This paper cites Adaptive streamline diffusion finite element methods for stationary convection-diffusion problems,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Adaptive streamline diffusion finite element methods for stationary convection-diffusion problems,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:18.747657Z

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Observation 268b2692-cbea-46bf-b17d-4a00e2a3d0f3 · outbound

This paper cites Adaptive finite element methods in computational mechanics,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Adaptive finite element methods in computational mechanics,

Reference 38

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This paper cites Computational differential equations. by k. eriksson, d. estep, p. hansbo & c. johnson. cambridge university press, 1997. 538 pp. isbn 0521 56738 6. £29.95,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Computational differential equations. by k. eriksson, d. estep, p. hansbo & c. johnson. cambridge university press, 1997. 538 pp. isbn 0521 56738 6. £29.95,

Reference 39

Resolution: verified exact; doi, observed 2026-08-15T22:46:18.122486Z

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Observation 5649f691-1c30-4783-b2a6-f4e8859650dd · outbound

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PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Unresolved cited work

Reference 40

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Observation dce7aa1e-e1b1-4e99-af08-189625135bf6 · outbound

This paper cites A generalized- α method for integrating the filtered navier–stokes equations with a stabilized finite element method,.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations A generalized- α method for integrating the filtered navier–stokes equations with a stabilized finite element method,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:18.725409Z

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Observation 4fbc9ccc-8666-4767-834f-fafcad8bd6e1 · outbound

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PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Unresolved cited work

Reference 42

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Observation c0e7be15-6445-4c2f-a666-330528999b43 · outbound

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PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Unresolved cited work

Reference 43

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:46:18.706354Z

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Observation ad73c420-eae9-4b9e-8172-9e3cf21cbf55 · outbound

This paper cites Iserles, A First Course in the Numerical Analysis of Differential Equations.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Iserles, A First Course in the Numerical Analysis of Differential Equations

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:18.696629Z

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Observation 9bb37ec8-50a5-4fd4-9e41-35f9e250ec15 · outbound

This paper cites S¨ uli and D.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations S¨ uli and D

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:18.687172Z

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Observation a16e063e-887a-48bc-9eed-c0f213c40121 · outbound

This paper cites Deep Residual Learning for Image Recognition.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Deep Residual Learning for Image Recognition

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-15T22:46:18.083744Z

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Observation b38216e1-3182-4a50-8414-92f1e48d5192 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Adam: A Method for Stochastic Optimization

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-15T22:46:18.087846Z

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Observation 5f0a23fd-e1a9-4190-a1e5-f5ecd3b3cd5a · outbound

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PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations Unresolved cited work

Reference 9991

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