Fetching the paper…
Reading the bibliography…
Climate simulations are essential in guiding our understanding of climate change and responding to its effects.
Achieving conservation of energy in neural network emulators for climate modeling, 2019
T. Beucler, S. Rasp, M. Pritchard, and P. Gentine · 1906
Earlier work this paper cites.
A comparative study of convolutional neural network models for wind field downscaling
K. Höhlein, M. Kern, T. Hewson, and R. Westermann · 1961
Earlier work this paper cites.
Bicubic spline interpolation
C. de Boor · 1962
Earlier work this paper cites.
Neural operator: Graph kernel network for partial differential equations, 2020
Z. Li, N. Kovachki, K. Azizzadenesheli, B. Liu, K. Bhattacharya, A. Stuart, and A. Anandkumar · 2003
Earlier work this paper cites.
Investigating two super-resolution methods for downscaling precipitation: Esrgan and car, 2020
C. D. Watson, C. Wang, T. Lynar, and K. Weldemariam · 2012
Earlier work this paper cites.
Generative adversarial networks, 2014
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Delving deeper into convolutional networks for learning video representations, 2015
N. Ballas, L. Yao, C. Pal, and A. Courville · 2015
Earlier work this paper cites.
Image super-resolution using deep convolutional networks, 2015
C. Dong, C. C. Loy, K. He, and X. Tang · 2015
Earlier work this paper cites.
Video frame synthesis using deep voxel flow
Z. Liu, R. A. Yeh, X. Tang, Y. Liu, and A. Agarwala · 2017
Earlier work this paper cites.
Esrgan: Enhanced super-resolution generative adversarial networks, 2018
X. Wang, K. Yu, S. Wu, J. Gu, Y. Liu, C. Dong, C. C. Loy, Y. Qiao, and X. Tang · 2018
Cited alongside, same era.
Climbing down charney’s ladder: machine learning and the post-dennard era of computational climate science
V. Balaji · 2020
Cited alongside, same era.
CliGAN: A structurally sensitive convolutional neural network model for statistical downscaling of precipitation from multi-model ensembles
C. Chaudhuri and C. Robertson · 2020
Cited alongside, same era.
Physics-guided architecture (PGA) of neural networks for quantifying uncertainty in lake temperature modeling
A. Daw, R. Q. Thomas, C. C. Carey, J. S. Read, A. P. Appling, and A. Karpatne · 2020
Cited alongside, same era.
ClimAlign: Unsupervised statistical downscaling of climate variables via normalizing flows
B. Groenke, L. Madaus, and C. Monteleoni · 2020
Enforcing analytic constraints in neural networks emulating physical systems
T. Beucler, M. Pritchard, S. Rasp, J. Ott, P. Baldi, and P. Gentine · 2021
Later among the works it cites.
Building high accuracy emulators for scientific simulations with deep neural architecture search
M. F. Kasim, D. Watson-Parris, L. Deaconu, S. Oliver, P. Hatfield, D. H. Froula, G. Gregori, M. Jarvis, S. Khatiwala, J. Korenaga, J. Topp-Mugglestone, E. Viezzer, and S. M. Vinko · 2021
Later among the works it cites.
Fourier neural operator for parametric partial differential equations
Z. Li, N. B. Kovachki, K. Azizzadenesheli, B. liu, K. Bhattacharya, A. Stuart, and A. Anandkumar · 2021
Later among the works it cites.
Spatio-temporal downscaling of climate data using convolutional and error-predicting neural networks
A. Serifi, T. Günther, and N. Ban · 2021
Later among the works it cites.
Development of a deep learning emulator for a distributed groundwater–surface water model: ParFlow-ML
H. Tran, E. Leonarduzzi, L. D. la Fuente, R. B. Hull, V. Bansal, C. Chennault, P. Gentine, P. Melchior, L. E. Condon, and R. M. Maxwell · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
The era5 global reanalysis
H. Hersbach, B. Bell, P. Berrisford, S. Hirahara, A. Horányi, J. Muñoz-Sabater, J. Nicolas, C. Peubey, R. Radu, D. Schepers, et al · 2020
Cited alongside, same era.
Stochastic super-resolution for downscaling time-evolving atmospheric fields with a generative adversarial network
J. Leinonen, D. Nerini, and A. Berne · 2020
Cited alongside, same era.
The hemispheric contrast in cloud microphysical properties constrains aerosol forcing
I. L. McCoy, D. T. McCoy, R. Wood, L. Regayre, D. Watson-Parris, D. P. Grosvenor, J. P. Mulcahy, Y. Hu, F. A.-M. Bender, P. R. Field, et al · 2020
Cited alongside, same era.
Machine learning for weather and climate are worlds apart
D. Watson-Parris · 2020
Cited alongside, same era.
Physics-informed learning of aerosol microphysics, 2022a
P. Harder, D. Watson-Parris, P. Stier, D. Strassel, N. R. Gauger, and J. Keuper
Cited in the paper.
P. Harder, Q. Yang, V. Ramesh, P. Sattigeri, A. Hernandez-Garcia, C. Watson, D. Szwarcman, and D. Rolnick
Cited in the paper.
Later among the works it cites.
Rainnet: A large-scale imagery dataset and benchmark for spatial precipitation downscaling, 2022
X. Chen, K. Feng, N. Liu, B. Ni, Y. Lu, Z. Tong, and Z. Liu · 2022
Later among the works it cites.
Increasing the accuracy and resolution of precipitation forecasts using deep generative models, 2022
I. Price and S. Rasp · 2022
Later among the works it cites.
Physics-constrained deep learning for climate downscaling, 2023
P. Harder, V. Ramesh, A. Hernandez-Garcia, Q. Yang, P. Sattigeri, D. Szwarcman, C. Watson, and D. Rolnick · 2023
Closest in time.
Neural operator: Learning maps between function spaces, 2023
N. Kovachki, Z. Li, B. Liu, K. Azizzadenesheli, K. Bhattacharya, A. Stuart, and A. Anandkumar · 2023
Closest in time.