The Grenoble Artificial Intelligence for Physical Sciences (GAP 2024) workshop will explore the intersections between machine learning research and the study of physical systems described by systems of differential equations.
We expect to gather researchers from machine learning, computational physics and diverse application fields, in order to foster collaborations and strengthen connections within the scientific community, and in particular across the alpine region. Application fields will include climate science, geophysics, solid state physics, and neuroscience.
Attendance and registration
Registration for attending the workshop on-site (including Julia Tutorial) are closed.
Ronan Fablet | IMT Atlantique (End-to-end neural data assimilation)
All presentations will provide a comprehensive overview of the field, equipping participants with the knowledge they need to delve into the specific applications and potential of machine learning in their respective domains.
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