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 Tübingen
- 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
Click here to sign up, or scan the QR-code below.
For any questions, please contact: j.m.aiken@mn.uio.no
