20232024

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Kevin Griffin researches on-the-fly machine learning, high-performance computing, and neuromorphic/energy-efficient computing; and he deploys these capabilities across various energy systems applications. Griffin is also developing turbulence models for predicting aerodynamic loads on wind turbines to inform structural design and improve the reliability of wind energy systems.

Research Interests

Multi-fidelity simulation

Neuromorphic computing

Data-driven modeling

Energy-efficient computing

Turbulence modeling

Computational fluid dynamics

Education/Academic Qualification

Bachelor, Mechanical and Aerospace Engineering, Princeton University

PhD, Mechanical Engineering, Stanford University

Master, Mechanical Engineering, Stanford University

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Collaborations and Top Research Areas From the Past 5 Years

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