The rapid growth of missions, observatories, and monitoring systems in the heliosphere, across the Solar System and from terrestrial or airborne facilities has created an unprecedented volume and diversity of data. Making sense of these observations requires methods that can both process large datasets efficiently and extract meaningful physical insight. Machine learning has become an important tool in this effort, complementing established physics-based approaches by enabling new ways of discovering patterns, building predictive models, and working with complex or incomplete measurements.
In recent years, increasing attention has been given to hybrid methods that combine machine learning with physical models. These approaches are now being applied across planetary and heliophysical domains, from forecasting solar eruptions and solar wind conditions, to automating the analysis of planetary surfaces or improving on-board data handling. They demonstrate how data-driven methods can benefit from physical knowledge, while physics-based models can be improved through modern data analysis techniques.
This session aims to provide an inclusive and interdisciplinary forum for researchers applying machine learning in planetary sciences and heliophysics, as well as those developing methods at the intersection between data-driven and physics-based approaches. We particularly encourage contributions that illustrate the wide range of applications, encourage exchange between disciplines and showcase the transition from research to operations.