AI/ML shows promise for accelerating Bayesian inversions, is able to capture and identify aspects of the dynamics of high fidelity models, and appears to improve complex system analysis, including extending predictive horizons, for reasons that are not fully understood. This has been demonstrated across the physical sciences including for geophysical modeling, fracture mechanics, and climate and weather prediction.
This conference focuses on efforts to advance the use of AI/ML to understand physical processes in the geosciences and adjacent fields, and brings together practitioners and theorists from academia and industry for an open exchange of current results and discussions of future strategies.
A central goal is to encourage diversity and collaboration between different disciplines, all through AI/ML practice, and to identify new connections between applications in fields, from networks to earthquakes, and turbulence to chaos.
There will be a mix of invited lectures, poster presentations, and open discussion.
Keynote lecturers include:
Anders Malthe-Sørenssen Department of Physics, University of Oslo
Caterina De Bacco MPI for Intelligent Systems, University of Tubingen
Ching-Yao Lai Geophysics and Computational Engineering, Stanford
Danny Caballero Physics and Computational Math, Michigan State University
Felix Kohler Expert Analytics
Joachim Mathiesen Niels Bohr Institute
Karianne Bergen Data, Earth, and Computer Science, Brown University
Nikola Kovachki Nvidia, NYU
Omar Ghattas Oden Institute, The University of Texas at Austin
Pia Zacharias Statkraft
William Gilpin Department of Physics, The University of Texas at Austin