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Analog forecasting of extreme-causing weather patterns using deep learning, , and . Journal of Advances in Modeling Earth Systems, 12 (2): e2019MS001958 (2020)Towards physically consistent data-driven weather forecasting: Integrating data assimilation with equivariance-preserving deep spatial transformers., , , , and . CoRR, (2021)Learning Closed-form Equations for Subgrid-scale Closures from High-fidelity Data: Promises and Challenges., , , , , and . CoRR, (2023)Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dynamical systems., , , and . J. Comput. Phys., (March 2023)Long-term instabilities of deep learning-based digital twins of the climate system: The cause and a solution., and . CoRR, (2023)FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators., , , , , , , , , and 3 other author(s). CoRR, (2022)A test case for application of convolutional neural networks to spatio-temporal climate data: Re-identifying clustered weather patterns., , and . CoRR, (2018)Analog forecasting of extreme-causing weather patterns using deep learning., , and . CoRR, (2019)A framework to integrate MFiX with Trilinos for high fidelity fluidized bed computations., , , and . HPEC, page 1-6. IEEE, (2016)OceanNet: A principled neural operator-based digital twin for regional oceans., , , , and . CoRR, (2023)