Paper: Quantifying Topological Uncertainty in Fractured Systems using Graph Theory and Machine Learning
Authors: Gowri Srinivasan, Jeffrey D. Hyman, David A. Osthus, Bryan A. Moore, Daniel O’Malley, Satish Karra, Esteban Rougier, Aric A. Hagberg, Abigail Hunter & Hari S. Viswanathan
Geophysics problems are as difficult as Nobel Prize-winning physics problems.
Dr. Jérõme A.R. Noir
This quote from Dr. Jérõme Noir has stayed with me throughout my career. The idea: while physicists face extreme math, but also have extremely precise data for unknown phenomena, geoscientists must find vital solutions for known phenomena using just a few data points on a planet. With very little data, how can complex problems in geoscience be solved? And, how do we assess the risk of being wrong? An uncertainty quantification framework recently developed by researchers at Los Alamos National Lab uses machine learning to help geoscientists arrive at quality decisions using limited data.
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