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We introduce a two-stage probabilistic framework for statistical downscaling using unpaired data.
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Integrated modelling of climate change impacts on water resources and quality in a lowland catchment: River kennet, uk
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Automated regression-based statistical downscaling tool
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On the need for bias correction of regional climate change projections of temperature and precipitation
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A comparison of statistical downscaling methods suited for wildfire applications
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Two-Dimensional Turbulence
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Bias correction, quantile mapping, and downscaling: Revisiting the inflation issue
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Learning a deep convolutional network for image super-resolution
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An intercomparison of statistical downscaling methods used for water resource assessments in the united states
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Projecting regional change
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Assessment of alternative methods for statistically downscaling daily gcm precipitation outputs to simulate regional streamflow
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Optimal transport for image processing
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Multivariate density estimation: theory, practice, and visualization
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Evaluating the stationarity assumption in statistically downscaled climate projections: is past performance an indicator of future results?
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Optimal transport for domain adaptation
R. Flamary, N. Courty, D. Tuia, and A. Rakotomamonjy · 2016
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M. Perrot, N. Courty, R. Flamary, and A. Habrard · 2016
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Data-driven optimal transport
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Near-linear time approximation algorithms for optimal transport via sinkhorn iteration
J. Altschuler, J. Niles-Weed, and P. Rigollet · 2017
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Joint distribution optimal transportation for domain adaptation
N. Courty, R. Flamary, A. Habrard, and A. Rakotomamonjy · 2017
Climalign: Unsupervised statistical downscaling of climate variables via normalizing flows
B. Groenke, L. Madaus, and C. Monteleoni · 2021
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SNIPS: Solving noisy inverse problems stochastically
B. Kawar, G. Vaksman, and M. Elad · 2021
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Machine learning–accelerated computational fluid dynamics
D. Kochkov, J. A. Smith, A. Alieva, Q. Wang, M. P. Brenner, and S. Hoyer · 2021
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SDEdit: Guided image synthesis and editing with stochastic differential equations
C. Meng, Y. He, Y. Song, J. Song, J. Wu, J.-Y. Zhu, and S. Ermon · 2021
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Learning to correct climate projection biases
B. Pan, G. J. Anderson, A. Goncalves, D. D. Lucas, C. J. Bonfils, J. Lee, Y. Tian, and H.-Y. Ma · 2021
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Climate goals and computing the future of clouds
T. Schneider, J. Teixeira, C. S. Bretherton, F. Brient, K. G. Pressel, C. Schär, and A. P. Siebesma · 2017
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Deepsd: Generating high resolution climate change projections through single image super-resolution
T. Vandal, E. Kodra, S. Ganguly, A. Michaelis, R. Nemani, and A. R. Ganguly · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros · 2017
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Perfect Prognosis , page 141–169
D. Maraun and M. Widmann · 2018
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Alignflow: Cycle consistent learning from multiple domains via normalizing flows, 2019
A. Grover, C. Chute, R. Shu, Z. Cao, and S. Ermon · 2019
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Wasserstein-2 generative networks
A. Korotin, V. Egiazarian, A. Asadulaev, A. Safin, and E. Burnaev · 2019
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A.-A. Pooladian and J. Niles-Weed · 2021
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Unit-ddpm: Unpaired image translation with denoising diffusion probabilistic models
H. Sasaki, C. G. Willcocks, and T. P. Breckon · 2021
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Are general circulation models obsolete?
V. Balaji, F. Couvreux, J. Deshayes, J. Gautrais, F. Hourdin, and C. Rio · 2022
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Perception prioritized training of diffusion models
J. Choi, J. Lee, C. Shin, S. Kim, H. Kim, and S. Yoon · 2022
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Optimal transport tools (ott): A jax toolbox for all things wasserstein
M. Cuturi, L. Meng-Papaxanthos, Y. Tian, C. Bunne, G. Davis, and O. Teboul · 2022
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Learning to correct spectral methods for simulating turbulent flows, 2022
G. Dresdner, D. Kochkov, P. Norgaard, L. Zepeda-Núñez, J. A. Smith, M. P. Brenner, and S. Hoyer · 2022
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Cdanet: A physics-informed deep neural network for downscaling fluid flows
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Generating physically-consistent high-resolution climate data with hard-constrained neural networks
P. Harder, Q. Yang, V. Ramesh, P. Sattigeri, A. Hernandez-Garcia, C. Watson, D. Szwarcman, and D. Rolnick · 2022
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A generative deep learning approach to stochastic downscaling of precipitation forecasts
L. Harris, A. T. McRae, M. Chantry, P. D. Dueben, and T. N. Palmer · 2022
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Elucidating the design space of diffusion-based generative models
T. Karras, M. Aittala, T. Aila, and S. Laine · 2022
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Denoising diffusion restoration models
B. Kawar, M. Elad, S. Ermon, and J. Song · 2022
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Srdiff: Single image super-resolution with diffusion probabilistic models
H. Li, Y. Yang, M. Chang, S. Chen, H. Feng, Z. Xu, Q. Li, and Y. Chen · 2022
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Statistical downscaling in maximum temperature future climatology
N. Naveena, G. C. Satyanarayana, N. Umakanth, M. C. Rao, B. Avinash, J. Jaswanth, and M. S. S. Reddy · 2022
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Increasing the accuracy and resolution of precipitation forecasts using deep generative models
I. Price and S. Rasp · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, K. Ghasemipour, R. Gontijo Lopes, B. Karagol Ayan, T. Salimans, et al · 2022
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Generative adversarial networks for image super-resolution: A survey
C. Tian, X. Zhang, J. C.-W. Lin, W. Zuo, and Y. Zhang · 2022
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Unifying diffusion models’ latent space, with applications to cyclediffusion and guidance
C. H. Wu and F. De la Torre · 2022
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Egsde: Unpaired image-to-image translation via energy-guided stochastic differential equations
M. Zhao, F. Bao, C. Li, and J. Zhu · 2022
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Unpaired downscaling of fluid flows with diffusion bridges
T. Bischoff and K. Deck · 2023
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User-defined event sampling and uncertainty quantification in diffusion models for physical dynamical systems
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A physics-informed diffusion model for high-fidelity flow field reconstruction
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Dual diffusion implicit bridges for image-to-image translation
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