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One of the guiding principles for designing AI-based weather forecasting systems is to embed physical constraints as inductive priors in the neural network architecture.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Perez, E., Strub, F., De Vries, H., Dumoulin, V., and Courville, A · 2018
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Axial attention in multidimensional transformers
Ho, J., Kalchbrenner, N., Weissenborn, D., and Salimans, T · 2019
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The era5 global reanalysis
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Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B · 2021
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Pathak, J., Subramanian, S., Harrington, P., Raja, S., Chattopadhyay, A., Mardani, M., Kurth, T., Hall, D., Li, Z., Azizzadenesheli, K., et al · 2022
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Kochkov, D., Yuval, J., Langmore, I., Norgaard, P., Smith, J., Mooers, G., Lottes, J., Rasp, S., Düben, P., Klöwer, M., et al · 2023
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Lessig, C., Luise, I., Gong, B., Langguth, M., Stadler, S., and Schultz, M · 2023
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Seeds: Emulation of weather forecast ensembles with diffusion models
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Scaling transformer neural networks for skillful and reliable medium-range weather forecasting
Nguyen, T., Shah, R., Bansal, H., Arcomano, T., Madireddy, S., Maulik, R., Kotamarthi, V., Foster, I., and Grover, A · 2023
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Andrychowicz, M., Espeholt, L., Li, D., Merchant, S., Merose, A., Zyda, F., Agrawal, S., and Kalchbrenner, N · 2023
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Fuxi: a cascade machine learning forecasting system for 15-day global weather forecast
Chen, L., Zhong, X., Zhang, F., Cheng, Y., Xu, Y., Qi, Y., and Li, H · 2023
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Esteves, C., Slotine, J.-J., and Makadia, A · 2023
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Rasp, S., Hoyer, S., Merose, A., Langmore, I., Battaglia, P., Russel, T., Sanchez-Gonzalez, A., Yang, V., Carver, R., Agrawal, S., et al · 2023
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Fourcastnext: Improving fourcastnet training with limited compute
Guo, E., Ahmed, M., Sun, Y., Mahendru, R., Yang, R., Cook, H., Leeuwenburg, T., and Evans, B · 2024
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