MITM12

Artificial intelligence (AI) and Machine Learning (ML) are rapidly transforming planetary science, enabling the analysis and interpretation of increasingly large, complex, and heterogeneous datasets from current and upcoming missions. Recent advances, such as deep learning, Large Language Models, generative AI, and physics-informed ML, are consistently opening new routes for discovery, allowing for new breakthroughs in fields as diverse as dynamical and observational astronomy, planetary space missions, and astrodynamics.

At the same time, there is a growing focus on model interpretability, uncertainty quantification, physical consistency, and reproducibility to make sure that AI-driven methods lead to strong and reliable scientific knowledge.

This session provides a forum for presenting and discussing state-of-the-art applications of AI and ML across planetary science, as well as emerging methodologies, best practices, and future directions at the interface of data-driven and physics-based modeling.