This session explores the use of artificial intelligence (AI) in ocean modeling, highlighting both advances and ongoing challenges in data, methods, and real-world applications. We invite abstracts on topics including: (1) collection and curation of large, diverse datasets for data-driven ocean modeling; (2) development and deployment of both AI-based and hybrid physics–AI approaches for ocean emulation and prediction; (3) integration of multi-source datasets for model improvement such as data assimilation, bias correction, and data fusion; (4) descriptive and process-oriented analysis such as classification and event detection; (5) decision-relevant model assessments, including uncertainty quantification, model interpretability, and evaluation of trustworthiness in AI/ML models; (6) real-world applications of AI-driven ocean models across public and private sectors; and (7) emerging approaches that support modeling workflow, multimodal integration, and stakeholder engagement, such as foundation models and large language models.

• IN - Informatics