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Elizabeth A. Barnes
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2020 – today
- 2024
- [j3]Amy McGovern
, Imme Ebert-Uphoff
AI2ES: The NSF AI Institute for Research on Trustworthy AI for Weather, Climate, and Coastal Oceanography. AI Mag. 45(1): 105-110 (2024) - 2023
- [c2]Sungduk Yu, Walter M. Hannah, Liran Peng, Jerry Lin, Mohamed Aziz Bhouri, Ritwik Gupta, Björn Lütjens, Justus C. Will, Gunnar Behrens, Julius Busecke, Nora Loose, Charles Stern, Tom Beucler, Bryce E. Harrop, Benjamin R. Hillman, Andrea M. Jenney, Savannah L. Ferretti, Nana Liu, Animashree Anandkumar, Noah D. Brenowitz, Veronika Eyring, Nicholas Geneva, Pierre Gentine, Stephan Mandt, Jaideep Pathak, Akshay Subramaniam, Carl Vondrick, Rose Yu, Laure Zanna, Tian Zheng, Ryan Abernathey, Fiaz Ahmed, David C. Bader, Pierre Baldi, Elizabeth A. Barnes, Christopher S. Bretherton, Peter M. Caldwell, Wayne Chuang, Yilun Han, Yu Huang, Fernando Iglesias-Suarez, Sanket R. Jantre, Karthik Kashinath, Marat Khairoutdinov, Thorsten Kurth, Nicholas J. Lutsko, Po-Lun Ma, Griffin Mooers, J. David Neelin, David A. Randall, Sara Shamekh, Mark Taylor, Nathan M. Urban, Janni Yuval, Guang Zhang, Mike Pritchard:
ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation. NeurIPS 2023 - [i8]Sungduk Yu, Walter M. Hannah, Liran Peng, Mohamed Aziz Bhouri, Ritwik Gupta, Jerry Lin, Björn Lütjens, Justus C. Will, Tom Beucler
ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators. CoRR abs/2306.08754 (2023) - 2022
- [i7]Antonios Mamalakis, Elizabeth A. Barnes, Imme Ebert-Uphoff:
Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscience. CoRR abs/2202.03407 (2022) - [i6]Antonios Mamalakis, Elizabeth A. Barnes, Imme Ebert-Uphoff:
Carefully choose the baseline: Lessons learned from applying XAI attribution methods for regression tasks in geoscience. CoRR abs/2208.09473 (2022) - 2021
- [j2]Christopher Irrgang
Towards neural Earth system modelling by integrating artificial intelligence in Earth system science. Nat. Mach. Intell. 3(8): 667-674 (2021) - [i5]Christopher Irrgang, Niklas Boers, Maike Sonnewald, Elizabeth A. Barnes, Christopher Kadow, Joanna Staneva, Jan Saynisch-Wagner:
Will Artificial Intelligence supersede Earth System and Climate Models? CoRR abs/2101.09126 (2021) - [i4]Antonios Mamalakis, Imme Ebert-Uphoff, Elizabeth A. Barnes:
Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark Dataset. CoRR abs/2103.10005 (2021) - [i3]Elizabeth A. Barnes, Randal J. Barnes:
Controlled abstention neural networks for identifying skillful predictions for regression problems. CoRR abs/2104.08236 (2021) - [i2]Elizabeth A. Barnes, Randal J. Barnes:
Controlled abstention neural networks for identifying skillful predictions for classification problems. CoRR abs/2104.08281 (2021) - 2020
- [c1]Antonios Mamalakis
Explainable Artificial Intelligence in Meteorology and Climate Science: Model Fine-Tuning, Calibrating Trust and Learning New Science. xxAI@ICML 2020: 315-339
2010 – 2019
- 2019
- [i1]Benjamin A. Toms, Elizabeth A. Barnes
Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability. CoRR abs/1912.01752 (2019) - 2017
- [j1]G. Brooke Anderson, Colin Eason, Elizabeth A. Barnes
Working with Daily Climate Model Output Data in R and the futureheatwaves Package. R J. 9(1): 124 (2017)
Coauthor Index
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