AI that learns how the universe flows — from ocean currents to stellar plasma
This advanced course explores Cross-Domain Foundation Models designed to understand and predict continuum dynamics across physical systems. By combining physics-based modeling, machine learning, and AI, learners will discover how a single intelligent model can generalize flow behavior across vastly different domains — from atmospheric and ocean circulation to combustion systems and astrophysical plasma.
The course focuses on how AI can learn governing principles such as conservation laws, turbulence structures, and multi-scale interactions, enabling faster, more accurate simulations beyond traditional CFD limitations.
What You Will Learn
Fundamentals of continuum mechanics and governing flow equations
Concept of foundation models applied to physical systems
Cross-domain learning: transferring knowledge between fluids, gases, and plasma
AI-driven discovery of hidden flow rules and patterns
Integration of physics-informed neural networks (PINNs) and data-driven models
Applications in climate science, aerospace, energy, and astrophysics
Applications Covered
Ocean and atmospheric circulation modeling
Urban airflow and environmental dynamics
Turbulence and multiphase flow systems
Combustion and energy systems
Plasma flows in fusion devices and stars
Who Should Enroll
CFD and simulation engineers
AI/ML professionals in scientific computing
Researchers in fluid mechanics, climate science, or astrophysics
Graduate students and advanced learners in engineering and physics