Joint Session 37 Physical Interpretability in Machine Learning

Wednesday, 15 January 2020: 8:30 AM-10:00 AM
260 (Boston Convention and Exhibition Center)
Hosts: (Joint between the 26th Conference on Probability and Statistics; the 19th Conference on Artificial Intelligence for Environmental Science; and the 30th Conference on Weather Analysis and Forecasting (WAF)/26th Conference on Numerical Weather Prediction (NWP) )
Elizabeth Satterfield, NRL, Monterey, CA and Philippe Tissot, Texas A&M Univ. - Corpus Christi, Conrad Blucher Institute, Corpus Christi, TX

Physical interpretability in machine learning

8:30 AM
Multiresolution Cluster Analysis—Addressing Trust in Climate Classification
Derek DeSantis, LANL, Los Alamos, NM; and P. Wolfram and B. Alexandrov
8:45 AM
Understanding What Deep Learning Has Learned about Tornadoes
Ryan A. Lagerquist, CIMMS, Norman, OK; and A. McGovern, D. J. Gagne II, C. R. Homeyer, and T. M. Smith
9:00 AM
Selected Methods from Explainable AI to Improve Understanding of Neural Network Reasoning for Environmental Science Applications
Imme Ebert-Uphoff, CIRA–Colorado State Univ., Fort Collins, CO; and K. Hilburn, B. A. Toms, and E. A. Barnes
9:15 AM
Emulation of Bin Microphysical Processes with Machine Learning
David John Gagne II, NCAR, Boulder, CO; and C. C. Chen and A. Gettelman
9:30 AM
Using Physically Interpretable Neural Networks to Discover Modes of Climate and Weather Variability
Benjamin A. Toms, Colorado State Univ., Fort Collins, CO; and E. A. Barnes and I. Ebert-Uphoff
- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner