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We present a super-resolution model for an advection-diffusion process with limited information.
Image super-resolution using deep convolutional networks
Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang · 2015
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Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
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Training generative neural networks via maximum mean discrepancy optimization
Gintare Karolina Dziugaite, Daniel M Roy, and Zoubin Ghahramani · 2015
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Deepsd: Generating high resolution climate change projections through single image super-resolution
Thomas Vandal, Evan Kodra, Sangram Ganguly, Andrew Michaelis, Ramakrishna Nemani, and Auroop R Ganguly · 2017
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Esrgan: Enhanced super-resolution generative adversarial networks
Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao, and Chen Change Loy · 2018
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Mocogan: Decomposing motion and content for video generation
Sergey Tulyakov, Ming-Yu Liu, Xiaodong Yang, and Jan Kautz · 2018
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tempogan: A temporally coherent, volumetric gan for super-resolution fluid flow
You Xie, Erik Franz, Mengyu Chu, and Nils Thuerey · 2018
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan, Chelsea Finn, Sergey Levine, Laurent Dinh, and Durk Kingma · 2019
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Kyongmin Yeo, Youngdeok Hwang, Xiao Liu, and Jayant Kalagnanama · 2019
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Srflow: Learning the super-resolution space with normalizing flow
Andreas Lugmayr, Martin Danelljan, Luc Van Gool, and Radu Timofte · 2020
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Pulse: Self-supervised photo upsampling via latent space exploration of generative models
Sachit Menon, Alexandru Damian, Shijia Hu, Nikhil Ravi, and Cynthia Rudin · 2020
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Disentangling physical dynamics from unknown factors for unsupervised video prediction
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Metnet: A neural weather model for precipitation forecasting
Casper Kaae Sønderby, Lasse Espeholt, Jonathan Heek, Mostafa Dehghani, Avital Oliver, Tim Salimans, Shreya Agrawal, Jason Hickey, and Nal Kalchbrenner · 2020
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Towards physics-informed deep learning for turbulent flow prediction
Rui Wang, Karthik Kashinath, Mustafa Mustafa, Adrian Albert, and Rose Yu · 2020
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Meshfreeflownet: a physics-constrained deep continuous space-time super-resolution framework
Soheil Esmaeilzadeh, Kamyar Azizzadenesheli, Karthik Kashinath, Mustafa Mustafa, Hamdi A Tchelepi, Philip Marcus, Mr Prabhat, Anima Anandkumar, et al · 2020
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Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels
Han Gao, Luning Sun, and Jian-Xun Wang · 2021
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