Algorithmic and AI-Driven Approaches to Ocean Digital Twins
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".
Deadline for manuscript submissions: 28 February 2027 | Viewed by 87
Editor
Interests: AI and its applications (health; human–machine interfaces; bioacoustics)
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Digital Twins of the Ocean (DTOs) are emerging as powerful computational frameworks for representing, monitoring, and predicting the behavior of complex marine environments through the integration of observational data, numerical models, and advanced analytics. Recent advances in AI, machine learning, data assimilation, and high-performance computing have significantly enhanced the capability of DTOs to process heterogeneous, high-dimensional, and continuously evolving ocean data. This Special Issue focuses on algorithmic and AI-driven approaches that improve the accuracy, scalability, and real-time responsiveness of digital twins. Topics of interest include physics-informed and hybrid AI models, machine learning for ocean state estimation and forecasting, sensor fusion, uncertainty quantification, surrogate modeling, reinforcement learning, and intelligent decision-support systems. Contributions addressing distributed edge–cloud architectures, autonomous data processing, and trustworthy AI for marine applications are particularly encouraged. By advancing computational methodologies, this Special Issue aims to support sustainable ocean management, climate adaptation, maritime operations, and the broader digital transformation of ocean science.
Prof. Dr. Todor Ganchev
Guest Editor
Manuscript Submission Information
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Keywords
- physics-informed modeling
- ocean modeling and simulation
- real-time analytics
- predictive modeling
- cyber–physical systems
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