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ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators.

S. Yu, W. Hannah, L. Peng, M. Bhouri, R. Gupta, J. Lin, B. Lütjens, J. Will, T. Beucler, B. Harrop, B. Hillman, A. Jenney, S. Ferretti, N. Liu, A. Anandkumar, N. Brenowitz, V. Eyring, P. Gentine, S. Mandt, J. Pathak, C. Vondrick, R. Yu, L. Zanna, R. Abernathey, F. Ahmed, D. Bader, P. Baldi, E. Barnes, G. Behrens, C. Bretherton, J. Busecke, P. Caldwell, W. Chuang, Y. Han, Y. Huang, F. Iglesias-Suarez, S. Jantre, K. Kashinath, M. Khairoutdinov, T. Kurth, N. Lutsko, P. Ma, G. Mooers, J. Neelin, D. Randall, S. Shamekh, A. Subramaniam, M. Taylor, и et al.. CoRR, (2023)

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Другие публикации лиц с тем же именем

Response of the Midlatitude Jets, and of Their Variability, to Increased Greenhouse Gases in the CMIP5 ModelsE. Barnes, и L. Polvani. Journal of Climate, 26 (18): 7117--7135 (08.09.2013)Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscience.A. Mamalakis, E. Barnes, и I. Ebert-Uphoff. CoRR, (2022)AI2ES: The NSF AI Institute for Research on Trustworthy AI for Weather, Climate, and Coastal Oceanography.A. McGovern, I. Ebert-Uphoff, E. Barnes, A. Bostrom, M. Cains, P. Davis, J. Demuth, D. Diochnos, A. Fagg, P. Tissot и 2 other автор(ы). AI Mag., 45 (1): 105-110 (2024)Controlled abstention neural networks for identifying skillful predictions for regression problems.E. Barnes, и R. Barnes. CoRR, (2021)ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators.S. Yu, W. Hannah, L. Peng, M. Bhouri, R. Gupta, J. Lin, B. Lütjens, J. Will, T. Beucler, B. Harrop и 39 other автор(ы). CoRR, (2023)Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark Dataset.A. Mamalakis, I. Ebert-Uphoff, и E. Barnes. CoRR, (2021)Windows of Opportunity for Skillful Forecasts Subseasonal to Seasonal and BeyondA. Mariotti, C. Baggett, E. Barnes, E. Becker, A. Butler, D. Collins, P. Dirmeyer, L. Ferranti, N. Johnson, J. Jones и 8 other автор(ы). Bulletin of the American Meteorological Society, 101 (5): E608--E625 (мая 2020)Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability.B. Toms, E. Barnes, и I. Ebert-Uphoff. CoRR, (2019)Controlled abstention neural networks for identifying skillful predictions for classification problems.E. Barnes, и R. Barnes. CoRR, (2021)Carefully choose the baseline: Lessons learned from applying XAI attribution methods for regression tasks in geoscience.A. Mamalakis, E. Barnes, и I. Ebert-Uphoff. CoRR, (2022)

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