Advances in Machine Learning and AI Applications for Coastal and Offshore Environments

Machine Learning (ML) and Large Language Models (LLM) in Artificial Intelligence (AI) Applications in Coastal and Offshore Environments: Frameworks, Applications, and Case Studies, Current Trends, and Future Directions.

This Research Topic explores the latest advances in the application of ML and LLM AI techniques in furthering our understanding of science and engineering applications in coastal and offshore environments. Active areas of research include data analytics and training algorithms applicable to coastal and offshore processes, design, construction, and operation of infrastructure, risk management, adaptation, and resilience. Multi-disciplinary applications focusing on metocean data collection, compilation, and adaptation for ML/LLM training to optimize operations, dynamic and integrated multi-process models applied to the forecasting, maintenance, and resiliency of infrastructure are welcome.

Topics of interest include, but are not limited to, applications of ML and LLM AI in:

• Coastal and Offshore Processes
• Design, Operation, and Maintenance of Coastal and Offshore Infrastructure
• Metocean Processes
• Digital Twins
• Hardware and Software
• Frameworks and Training Algorithms
• Data Collection, Analytics, and Assimilation
• Hydrodynamics, Sediment Transport, and Morphodynamics
• Bathymetry and Topography Mapping
• Fluid–Structure Interactions
• Environmental Monitoring and Forecasting
• Adaptation and Resiliency
• Risk Assessment and Management

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Editorial
• FAIR² Data
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Editorial
• FAIR² Data
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review

Keywords: Coastal and offshore engineering, Artificial Intelligence, Machine Learning, Large language models, Marine Digitalization, Machine Learning (ML), Large Language Models (LLMs), Artificial Intelligence (AI), Coastal Engineering, Offshore Engineering, Coastal and Offshore Infrastructure, Metocean Data, Digital Twins, Data Analytics, Training Algorithms, Hydrodynamics, Sediment Transport, Morphodynamics, Fluid–Structure Interaction, Environmental Monitoring, Climate Adaptation, Resilience and Risk Management, Predictive Modelling, Operational Forecasting, Smart Infrastructure, Sustainable Design, Ocean Data Assimilation, Structural Health Monitoring, AI-Driven Decision Support

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.