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kdlamb committed Dec 6, 2024
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<td><strong>2024<BR><BR><BR><BR><BR></strong></td>
<td><strong>Discovering How Ice Crystals Grow Using Neural Ordinary Differential Equations and Symbolic Regression</strong><br>
<td><strong>Discovering How Ice Crystals Grow Using Neural Ordinary Differential Equations and Symbolic Regression <a href="https://ml4physicalsciences.github.io/2024/files/NeurIPS_ML4PS_2024_208.pdf">[preprint]</a></strong><br>
K.D. Lamb and J.Y. Harrington<br>
Accepted, Machine Learning for the Physical Sciences Workshop <br>
Machine Learning for the Physical Sciences Workshop <br>
2024 Conference on Neural Information Processing Systems<br></td>
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<td><strong>Spatio-Temporal Machine Learning Models for Emulation of Global Atmospheric Composition</strong><br>
<td><strong>Spatio-Temporal Machine Learning Models for Emulation of Global Atmospheric Composition </strong><br>
M. Erfani, K.D. Lamb, S.E. Bauer, K. Tsigaridis, M. van Lier-Walqui, G. Schmidt<br>
Accepted, Tackling Climate Change with Machine Learning Workshop <br>
Tackling Climate Change with Machine Learning Workshop <br>
2024 Conference on Neural Information Processing Systems<br></td>
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<td><strong>Representation of iron aerosol size distribution is critical in evaluating atmospheric soluble iron input to the ocean <a href="https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1454/">[preprint]</a></strong><BR>
<td><strong>Representation of iron aerosol size distribution is critical in evaluating atmospheric soluble iron input to the ocean <a href="https://acp.copernicus.org/articles/24/13115/2024/">[link]</a></strong><BR>
M. Liu, H. Matsui, D. Hamilton, S. Rathod, K.D. Lamb, N. Mahowald.<BR>
Accepted, <em> Atmospheric Chemistry and Physics</em> (2024)</td>
<em> Atmospheric Chemistry and Physics</em>, 24, 13115-13127 (2024)</td>
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