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1NeurIPS_ML4PS_2022_28.pdf
https://ml4physicalsciences.github.io/2022/files/NeurIPS_ML4PS_2022_28.pdf
a year ago by @annakrause
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1The key challenge for machine learning parameterizations of clouds - Stephan Rasp
https://raspstephan.github.io/blog/optimization-dichotomy/#
3 years ago by @annakrause
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3Phys. Rev. Lett. 120, 024102 (2018) - Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach
https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.120.024102
3 years ago by @annakrause
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1Data-driven surrogate models for climate modeling: application of echo state networks, RNN-LSTM and ANN to the multi-scale Lorenz system as a test case - paper.pdf
https://www.climatechange.ai/papers/icml2019/22/paper.pdf
4 years ago by @annakrause
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4GenCast: Diffusion-based ensemble forecasting for medium-range weather
I. Price, A. Sanchez-Gonzalez, F. Alet, T. Ewalds, A. El-Kadi, J. Stott, S. Mohamed, P. Battaglia, R. Lam, and M. Willson. (2023)
4 months ago by @annakrause
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4Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere
B. Bonev, T. Kurth, C. Hundt, J. Pathak, M. Baust, K. Kashinath, and A. Anandkumar. (2023)
5 months ago by @annakrause
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6GraphCast: Learning skillful medium-range global weather forecasting
R. Lam, A. Sanchez-Gonzalez, M. Willson, P. Wirnsberger, M. Fortunato, F. Alet, S. Ravuri, T. Ewalds, Z. Eaton-Rosen, W. Hu and 8 other author(s). (2022)cite arxiv:2212.12794Comment: GraphCast code and trained weights are available at: https://github.com/deepmind/graphcast.
6 months ago by @annakrause
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2FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead
K. Chen, T. Han, J. Gong, L. Bai, F. Ling, J. Luo, X. Chen, L. Ma, T. Zhang, R. Su and 4 other author(s). (2023)cite arxiv:2304.02948Comment: 12 pages.
6 months ago by @annakrause
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5Accurate medium-range global weather forecasting with 3D neural networks
K. Bi, L. Xie, H. Zhang, X. Chen, X. Gu, and Q. Tian. Nature, (July 2023)Publisher: Nature Publishing Group.
6 months ago by @annakrause
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1Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions
J. Yuval, and P. O’Gorman. Nature Communications, (2020)
7 months ago by @annakrause
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2ACE: A fast, skillful learned global atmospheric model for climate prediction
O. Watt-Meyer, G. Dresdner, J. McGibbon, S. Clark, B. Henn, J. Duncan, N. Brenowitz, K. Kashinath, M. Pritchard, B. Bonev and 2 other author(s). (2023)cite arxiv:2310.02074.
8 months ago by @annakrause
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1Wind-Topo: Downscaling near-surface wind fields to high-resolution topography in highly complex terrain with deep learning
J. Dujardin, and M. Lehning. Quarterly Journal of the Royal Meteorological Society, 148 (744): 1368-1388 (2022)
8 months ago by @annakrause
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3Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers
J. Guibas, M. Mardani, Z. Li, A. Tao, A. Anandkumar, and B. Catanzaro. (2021)cite arxiv:2111.13587.
9 months ago by @annakrause
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5FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators
J. Pathak, S. Subramanian, P. Harrington, S. Raja, A. Chattopadhyay, M. Mardani, T. Kurth, D. Hall, Z. Li, K. Azizzadenesheli and 3 other author(s). (February 2022)arXiv:2202.11214 physics.
9 months ago by @annakrause
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2Solving Differential Equations Using Neural Network Solution Bundles
C. Flamant, P. Protopapas, and D. Sondak. (2020)cite arxiv:2006.14372Comment: 21 pages, 12 figures, 10 tables.
2 years ago by @annakrause
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1The Monash Simple Climate Model experiments (MSCM-DB v1.0): an interactive database of mean climate, climate change, and scenario simulations
D. Dommenget, K. Nice, T. Bayr, D. Kasang, C. Stassen, and M. Rezny. Geoscientific Model Development, 12 (6): 2155--2179 (2019)
2 years ago by @annakrause
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1Data-driven predictions of a multiscale Lorenz 96 chaotic system using machine-learning methods: reservoir computing, artificial neural network, and long short-term memory network
A. Chattopadhyay, P. Hassanzadeh, and D. Subramanian. Nonlinear Processes in Geophysics, 27 (3): 373-389 (2019)
2 years ago by @annakrause
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2Neural Operator: Learning Maps Between Function Spaces
N. Kovachki, Z. Li, B. Liu, K. Azizzadenesheli, K. Bhattacharya, A. Stuart, and A. Anandkumar. (2021)cite arxiv:2108.08481.
2 years ago by @annakrause
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2Neural Operator: Graph Kernel Network for Partial Differential Equations
Z. Li, N. Kovachki, K. Azizzadenesheli, B. Liu, K. Bhattacharya, A. Stuart, and A. Anandkumar. (2020)cite arxiv:2003.03485.
2 years ago by @annakrause
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2A Fortran-Keras Deep Learning Bridge for Scientific Computing
J. Ott, M. Pritchard, N. Best, E. Linstead, M. Curcic, and P. Baldi. (2020)cite arxiv:2004.10652.
2 years ago by @annakrause
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1Generalization properties of feed-forward neural networks trained on Lorenz systems
S. Scher, and G. Messori. Nonlinear Processes in Geophysics, 26 (4): 381-399 (0000)
2 years ago by @annakrause
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3Challenges and design choices for global weather and climate models based on machine learning
P. Dueben, and P. Bauer. Geoscientific Model Development, 11 (10): 3999--4009 (2018)
2 years ago by @annakrause
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1Causal Discovery for Climate Research Using Graphical Models
I. Ebert-Uphoff, and Y. Deng. Journal of Climate, 25 (17): 5648 - 5665 (2012)
2 years ago by @annakrause
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2Robust Low-Rank Discovery of Data-Driven Partial Differential Equations
J. Li, G. Sun, G. Zhao, and L. wei H. Lehman. Proceedings of the AAAI Conference on Artificial Intelligence, 34 (01): 767--774 (April 2020)
3 years ago by @annakrause
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1NeurIPS_ML4PS_2022_28.pdf

1The key challenge for machine learning parameterizations of clouds - Stephan Rasp

3Phys. Rev. Lett. 120, 024102 (2018) - Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach

1Data-driven surrogate models for climate modeling: application of echo state networks, RNN-LSTM and ANN to the multi-scale Lorenz system as a test case - paper.pdf

publications (hide)33
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4GenCast: Diffusion-based ensemble forecasting for medium-range weather

4Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere

6GraphCast: Learning skillful medium-range global weather forecasting

2FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead

5Accurate medium-range global weather forecasting with 3D neural networks

1Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions

2ACE: A fast, skillful learned global atmospheric model for climate prediction

1Wind-Topo: Downscaling near-surface wind fields to high-resolution topography in highly complex terrain with deep learning

3Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers

5FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators

2Solving Differential Equations Using Neural Network Solution Bundles

1The Monash Simple Climate Model experiments (MSCM-DB v1.0): an interactive database of mean climate, climate change, and scenario simulations

1Data-driven predictions of a multiscale Lorenz 96 chaotic system using machine-learning methods: reservoir computing, artificial neural network, and long short-term memory network

2Neural Operator: Learning Maps Between Function Spaces

2Neural Operator: Graph Kernel Network for Partial Differential Equations

2A Fortran-Keras Deep Learning Bridge for Scientific Computing

1Generalization properties of feed-forward neural networks trained on Lorenz systems

3Challenges and design choices for global weather and climate models based on machine learning

1Causal Discovery for Climate Research Using Graphical Models

2Robust Low-Rank Discovery of Data-Driven Partial Differential Equations

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