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[paper] Cloud microphysics training and aerosol inference with the Fiats deep learning library #42

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@rouson

New Paper Submission

Paper Title: Cloud microphysics training and aerosol inference with the Fiats deep learning library

Authors: Damian Rouson, Dan Bonachea, Zhe Bai, Baboucarr Dibba, Ethan Gutmann, Katherine Rasmussen, David Torres

Repository: SEA-ISS-2025-Cloud-microphysics-training

Repository URL: https://github.com/BerkeleyLab/SEA-ISS-2025-Cloud-microphysics-training

Submission Checklist

  • The paper has been properly formatted according to the repository's guidelines.
  • All co-authors have reviewed and approved the submission.
  • Any necessary supporting files or data are included.
  • The paper adheres to any specific licensing requirements of the repository.
  • Any conflicts of interest or funding sources are disclosed.
  • The submission complies with ethical guidelines and standards.

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Contact Information

Corresponding Author: Damian Rouson

Email: rouson@lbl.gov

Affiliation: Lawrence Berkeley National Laboratory

ORCID ID: 0000-0002-2344-868X

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