Data-Driven and AI-Assisted Discovery of Geophysical Fluid Dynamics

Dear Colleagues,

The rapid expansion of high-resolution in situ and remote data, satellite imagery, autonomous underwater vehicles, and large-scale numerical ensembles has transitioned oceanography into a data-rich era. Data-driven modeling and artificial intelligence (AI) are now pivotal in deciphering the complex, multiscale, and nonlinear nature of ocean fluid dynamics. By integrating classical physical principles with advanced machine learning, researchers can accelerate the discovery of governing equations, optimize state estimation, and enhance predictive accuracy for phenomena that defy traditional analytical solutions.

This Special Issue of Fluids focuses on the “Data-Driven and AI-Assisted Discovery of Geophysical Fluid Dynamics.” We aim to highlight innovative methodologies that bridge the gap between “black-box” AI and physical interpretability. We invite contributions that explore the synergy between fluid physics and data science, including physics-informed neural networks, sparse system identification, and reduced-order modeling. This collection seeks to advance the frontier of oceanic research, providing robust tools for climate forecasting, coastal management, and the development of intelligent marine infrastructure.

Topics of interest include, but are not limited to, the following:

• Data-driven fluid and wave mechanics;
• Physics-informed neural networks (PINNs);
• Sparse identification of nonlinear dynamics (SINDy);
• Reduced-order modeling (ROM);
• Deep learning for ocean forecasting;
• Operator learning in fluid systems;
• Data assimilation and state estimation;
• Super-resolution and flow reconstruction;
• Interpretable machine learning;
• Autonomous underwater sensing.

Prof. Dr. Cihan BAYINDIR
Guest Editor

Manuscript Submission Information

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• oceanic fluid dynamics
• wave dynamics
• data-driven discovery of fluid dynamics
• physics-informed neural networks (PINNs)
• sparse identification of nonlinear dynamics (SINDy)