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Climate and weather prediction traditionally relies on complex numerical simulations of atmospheric physics.
The general circulation of the atmosphere: A numerical experiment
Norman A Phillips · 1956
Earlier work this paper cites.
A theoretical framework for back-propagation
Yann LeCun, D Touresky, G Hinton, and T Sejnowski · 1988
Earlier work this paper cites.
Localized precipitation forecasts from a numerical weather prediction model using artificial neural networks
Robert Kuligowski and Ana Barros · 1998
Earlier work this paper cites.
Atmospheric circulation dynamics and circulation models
Masaki Satoh · 2004
Earlier work this paper cites.
The origins of computer weather prediction and climate modeling
Peter Lynch · 2008
Earlier work this paper cites.
An efficient weather forecasting system using artificial neural network
Santhosh Baboo and Kadar Shereef · 2010
Earlier work this paper cites.
The numerical method of lines: integration of partial differential equations
William Schiesser · 2012
Earlier work this paper cites.
The community earth system model: a framework for collaborative research
James Hurrell, Marika Holland, Peter Gent, Steven Ghan, Jennifer Kay, Paul Kushner, J-F Lamarque, William Large, D Lawrence, Keith Lindsay, et al · 2013
Earlier work this paper cites.
A PDE perspective on climate modeling
Sofia Broomé and Jonathan Ridenour · 2014
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Discovering governing equations from data by sparse identification of nonlinear dynamical systems
Steven Brunton, Joshua Proctor, and Nathan Kutz · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Neural ordinary differential equations
Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud · 2018
Earlier work this paper cites.
GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series
Edward De Brouwer, Jaak Simm, Adam Arany, and Yves Moreau · 2019
Earlier work this paper cites.
Hamiltonian neural networks
Samuel Greydanus, Misko Dzamba, and Jason Yosinski · 2019
Earlier work this paper cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Earlier work this paper cites.
Graph neural ordinary differential equations
Michael Poli, Stefano Massaroli, Junyoung Park, Atsushi Yamashita, Hajime Asama, and Jinkyoo Park · 2019
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Maziar Raissi, Paris Perdikaris, and George E Karniadakis · 2019
Cited alongside, same era.
Latent ordinary differential equations for irregularly-sampled time series
Yulia Rubanova, Ricky TQ Chen, and David K Duvenaud · 2019
Cited alongside, same era.
ODE2VAE: Deep generative second order ODEs with Bayesian neural networks
Cagatay Yildiz, Markus Heinonen, and Harri Lahdesmaki · 2019
Cited alongside, same era.
Lagrangian neural networks
Miles Cranmer, Sam Greydanus, Stephan Hoyer, Peter Battaglia, David Spergel, and Shirley Ho · 2020
Cited alongside, same era.
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Salvatore Cuomo, Vincenzo Schiano Di Cola, Fabio Giampaolo, Gianluigi Rozza, Maziar Raissi, and Francesco Piccialli · 2022
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Deconstructing the inductive biases of Hamiltonian neural networks
Nate Gruver, Marc Finzi, Samuel Stanton, and Andrew Gordon Wilson · 2022
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Forecasting global weather with graph neural networks
Ryan Keisler · 2022
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GraphCast: Learning skillful medium-range global weather forecasting
Remi Lam, Alvaro Sanchez-Gonzalez, Matthew Willson, Peter Wirnsberger, Meire Fortunato, Alexander Pritzel, Suman Ravuri, Timo Ewalds, Ferran Alet, Zach Eaton-Rosen, et al · 2022
Later among the works it cites.
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Dissecting neural ODEs
Stefano Massaroli, Michael Poli, Jinkyoo Park, Atsushi Yamashita, and Hajime Asama · 2020
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Stephan Rasp, Peter Dueben, Sebastian Scher, Jonathan Weyn, Soukayna Mouatadid, and Nils Thuerey · 2020
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Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Machine learning–accelerated computational fluid dynamics
Dmitrii Kochkov, Jamie Smith, Ayya Alieva, Qing Wang, Michael Brenner, and Stephan Hoyer · 2021
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Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar · 2021
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FourCastNet: A global data-driven high-resolution weather model using adaptive fourier neural operators
Jaideep Pathak, Shashank Subramanian, Peter Harrington, Sanjeev Raja, Ashesh Chattopadhyay, Morteza Mardani, Thorsten Kurth, David Hall, Zongyi Li, Kamyar Azizzadenesheli, et al · 2022
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Accurate medium-range global weather forecasting with 3d neural networks
Kaifeng Bi, Lingxi Xie, Hengheng Zhang, Xin Chen, Xiaotao Gu, and Qi Tian · 2023
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Clifford neural layers for PDE modeling
Johannes Brandstetter, Rianne van den Berg, Max Welling, and Jayesh Gupta · 2023
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Climate modeling with neural advection–diffusion equation
Hwangyong Choi, Jeongwhan Choi, Jeehyun Hwang, Kookjin Lee, Dongeun Lee, and Noseong Park · 2023
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IFS Documentation CY48R1
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ClimaX: A foundation model for weather and climate
Tung Nguyen, Johannes Brandstetter, Ashish Kapoor, Jayesh K Gupta, and Aditya Grover · 2023
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Yuchen Zhang, Mingsheng Long, Kaiyuan Chen, Lanxiang Xing, Ronghua Jin, Michael Jordan, and Jianmin Wang · 2023
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