The recent growing number of probes in the heliosphere and future missions in preparation led to the current decade being labelled as "the golden age of heliophysics research". With more viewpoints and data downstreamed to Earth, machine learning (ML) has become a precious tool for planetary and heliospheric research to process the increasing amount of data and help the discovery and modelisation of physical systems. Recent years have also seen the development of novel approaches leveraging complex data representations with highly parameterised machine learning models and combining them with well-defined and understood physical models. These advancements in ML with physical insights or physically informed neural networks inspire new questions about how each field can respectively help develop the other. To better understand this intersection between data-driven learning approaches and physical models in planetary sciences and heliophysics, we seek to bring ML researchers and physical scientists together as part of this session and stimulate the interaction of both fields by presenting state-of-the-art approaches and cross-disciplinary visions of the field.

The "ML for Planetary Sciences and Heliophysics" session aims to provide an inclusive and cutting-edge space for discussions and exchanges at the intersection of machine learning, planetary and heliophysics topics. This space covers (1) the application of machine learning/deep learning to space research, (2) novel datasets and statistical data analysis methods over large data corpora, and (3) new approaches combining learning-based with physics-based to bring an understanding of the new AI-powered science and the resulting advancements in heliophysics research.
Topics of interest include all aspects of ML and heliophysics, including, but not limited to: space weather forecasting, computer vision systems applied to space data, time-series analysis of dynamical systems, new machine learning models and data-assimilation techniques, and physically informed models.