324 citations · 749 across the 13 of their papers we have counts for
6 papers · 1 filter
A Primer on Topological Data Analysis to Support Image Analysis Tasks in Environmental Science
Lander Ver Hoef, Henry Adams, Emily J. King +1
Topological data analysis (TDA) is a tool from data science and mathematics that is beginning to make waves in environmental science. In this work, we seek to provide an intuitive…
Can we integrate spatial verification methods into neural-network loss functions for atmospheric science?
Ryan Lagerquist, Imme Ebert-Uphoff
In the last decade, much work in atmospheric science has focused on spatial verification (SV) methods for gridded prediction, which overcome serious disadvantages of pixelwise veri…
CIRA Guide to Custom Loss Functions for Neural Networks in Environmental Sciences -- Version 1
Imme Ebert-Uphoff, Ryan Lagerquist, Kyle Hilburn +5
Neural networks are increasingly used in environmental science applications. Furthermore, neural network models are trained by minimizing a loss function, and it is crucial to choo…
Machine Learning for the Geosciences: Challenges and Opportunities
Anuj Karpatne, Imme Ebert-Uphoff, Sai Ravela +2
Geosciences is a field of great societal relevance that requires solutions to several urgent problems facing our humanity and the planet. As geosciences enters the era of big data,…
High-Dimensional Dependency Structure Learning for Physical Processes
Jamal Golmohammadi, Imme Ebert-Uphoff, Sijie He +2
In this paper, we consider the use of structure learning methods for probabilistic graphical models to identify statistical dependencies in high-dimensional physical processes. Suc…
Using Causal Discovery to Track Information Flow in Spatio-Temporal Data - A Testbed and Experimental Results Using Advection-Diffusion Simulations
Imme Ebert-Uphoff, Yi Deng
Causal discovery algorithms based on probabilistic graphical models have emerged in geoscience applications for the identification and visualization of dynamical processes. The key…