Machine Learning and Data Science - IAMA 2023

International Aerosol Modeling and Algorithms Conference 2023

Machine Learning and Data Science
Chaired by: Christopher Tessum, University of Illinois, Sam Silva, USC

0:00 - Introduction
Christopher Tessum

1:37 - Emulating Aerosol Optical Properties Using Machine Learning
Andrew Geiss, Pacific Northwest National Laboratory

19:48 - Physics-Constrained Learning of Aerosol Microphysics
Paula Harder, Fraunhofer ITWM

41:36 - Quantum Chemical Modelling of Atmospheric Molecular Clusters Enhanced by Machine Learning
Jakub Kubecka, Aarhus University

57:36 - Characterizing Atmospheric Molecules for Machine Learning
Hilda Sandström, Aalto University

1:15:01 - Combining Earth system modeling and machine learning to investigate volcanic sulfate deposition in polar ice cores
Kostas Tsigaridis, Columbia University and NASA GISS

1:31:52 - Data driven futures: From stakeholder development to model development
David Topping, University of Manchester

https://iama.aqrc.ucdavis.edu/2023-pr...