The rapid growth of missions, observatories, and monitoring systems across the heliosphere and Solar System, together with surface-based and airborne facilities, has created an unprecedented volume and diversity of data. Making sense of these observations requires methods that can efficiently process large datasets while extracting meaningful physical insight. Machine learning has become an important tool in this effort, complementing established physics-based approaches by offering new ways to identify patterns, develop predictive models, and work with complex or incomplete measurements.

In recent years, increasing attention has been given to hybrid approaches that combine machine learning with physical models. These methods are now being applied across planetary science and heliophysics, from forecasting solar eruptions and solar wind conditions to automating the analysis of planetary surfaces and improving on-board data processing. They demonstrate how data-driven methods can benefit from physical knowledge, while physics-based models can in turn be enhanced through modern data analysis techniques.

This session aims to provide an inclusive and interdisciplinary forum for researchers applying machine learning in planetary science and heliophysics, as well as for those developing methods at the interface between data-driven and physics-based approaches. We particularly encourage contributions that demonstrate the wide range of possible applications, foster exchange across disciplines, and showcase the transition of new methods from research to operations.