MITM5

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, offer innovative new tools to explore and interpret the big datasets which are ubiquitous across all domains of planetary and space science.
These tools have the potential to enable larger scale and more in-depth analyses than have ever been possible before. 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. Thanks to this, the scientific revenue from the application of such technologies is steadily increasing.
Historically ML techniques have had a high barrier to entry for planetary scientists. However, as the use of ML has become more widespread, the techniques have become more accessible, thereby democratising this powerful tool.
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 modelling. It welcomes contributions reporting original scientific results from AI-driven applications and discoveries from across the solar system and beyond. We particularly encourage the discussion of open access and transferable models, as well as presentations which will help promote these techniques to others who are considering using them.