Our Vision for JGR: Machine Learning and Computation by E. Camporeale, R. Marino, the Editorial Board

“…Journal of Geophysical Research: Machine Learning and Computation…”
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Cross‐Domain Foundation Model Adaptation: Pioneering Computer Vision Models for Geophysical Data Analysis by Zhixiang Guo, Xinming Wu, Luming Liang, Hanlin Sheng, Nuo Chen, Zhengfa Bi

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Computationally Efficient Hybrid Downscaling of Surf Zone Hydrodynamics: Methodology and Evaluation by E. R. Echevarria, S. Contardo, B. Pérez‐Díaz, R. K. Hoeke, B. Leighton, C. Trenham, L. Cagigal, F. J. Méndez

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Accelerating the Discovery of Steady‐States of Planetary Interior Dynamics With Machine Learning by Siddhant Agarwal, Nicola Tosi, Christian Hüttig, David S. Greenberg, Ali Can Bekar

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Seismic Wave Propagation Simulation With Quantum Computing by Xixiang Wen, Yanfei Wang

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How Does Neural Network Reparametrization Improve Geophysical Inversion? by Yuping Wu, Jianwei Ma

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TEEMLEAP—A New Testbed for Exploring Machine Learning in Atmospheric Prediction for Research and Education by J. Wilhelm, J. Quinting, M. Burba, S. Hollborn, U. Ehret, I. Pena Sánchez, S. Lerch, J. Meyer, B. Verfürth, P. Knippertz

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Machine Learning Potential for Serpentines by Hongjin Wang, Chenxing Luo, Renata M. Wentzcovitch

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Incorporating Deep Learning Into Hydrogeological Modeling: Advancements, Challenges, and Future Directions by Zhenxue Dai, Chuanjun Zhan, Huichao Yin, Junjun Chen, Lulu Xu, Yuzhou Xia, Songlin Yang, Wei Chen, Mingxu Cao, Zhengyang Du, Xiaoying Zhang, Bicheng Yan, Yue Ma, Hao Wang, Farzad Moeini, Mohamad Reza Soltanian, Hung Vo Thanh, Kenneth C. Carroll

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One‐Fit‐All Transformer for Multimodal Geophysical Inversion: Method and Application by Yiran Jiang, Jianwei Ma, Jieyuan Ning, Jiaqi Li, Han Wu, Tiezhao Bao

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Machine Learning Detection of Melting Layers From Radar Observations by Yan Xie, Fraser King, Claire Pettersen, Mark Flanner

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Evaluating Global Machine Learning Models for Tropical Cyclone Dynamics and Thermodynamics by Pankaj Lal Sahu, Sukumaran Sandeep, Hariprasad Kodamana

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Explainable Machine Learning for Mapping Minerals From CRISM Hyperspectral Data by Sandeepan Dhoundiyal, Moni Shankar Dey, Shashikant Singh, Pattathal V. Arun, Guneshwar Thangjam, Alok Porwal

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Recommendations for Comprehensive and Independent Evaluation of Machine Learning‐Based Earth System Models by P. A. Ullrich, E. A. Barnes, W. D. Collins, K. Dagon, S. Duan, J. Elms, J. Lee, L. R. Leung, D. Lu, M. J. Molina, T. A. O’Brien, F. O. Rebassoo

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Process‐Based Machine Learning Observationally Constrains Future Regional Warming Projections by Sophie Wilkinson, Peer Nowack, Manoj Joshi

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Detachment of the Subducting Slab in Ancient Subduction Zones Discerned Using Machine Learning by Quan Ou, Dongyu Zheng, Alexander R. L. Nichols, Kun Wang, Sheng‐Ping Qian, Feng Zi, Anlin Ma, Yun‐Chuan Zeng, Zongying Huang, Jia Liao, Zixi Hou

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Uncertainty Quantification of Geophysical and Hydrologic Parameters Estimated From Borehole Nuclear Magnetic Resonance Data by Burke J. Minsley, Stephanie N. Phillips, Stephanie R. James

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Homogeneity Equation for the Effective Elastic Properties of Heterogenous Rocks Developed From Machine Learning by E. Maalouf, H. Kareem, H. Chebli

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A Kp‐Driven Machine Learning Model Predicting the Ultraviolet Emission Auroral Oval by Huiting Feng, Dedong Wang, Yuri Y. Shprits, Artem Smirnov, Deyu Guo, Yoshizumi Miyoshi, Stefano Bianco, Shangchun Teng, Run Shi, Su Zhou, Yongliang Zhang

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Improving PM2.5 and Visibility Predictions in China Using Machine Learning and Ensemble Forecasting by Ziyin Zhang, Jing Xu, Xiujuan Zhao, Siyu Cheng

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