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DOE OSTI · 2481519

Physics-Reinforced Machine Learning Algorithms for Multiscale Closure Model Discovery

San, Omer [Oklahoma State Univ., Stillwater, OK (United States)]
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Abstract

The central objective of this project was to address the challenge of modeling and simulating complex multiscale turbulence phenomena by leveraging physics-guided machine learning (PGML) and hybrid modeling approaches. By integrating physics-based methods with data-driven models, the research focused on achieving robust and scalable solutions for geophysical turbulence, enhancing numerical weather prediction and climate research tools. The project resulted in significant advancements in computational modeling paradigms, predictive tools for reduced-order modeling, and innovative algorithms for fluid dynamics.

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71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS97 MATHEMATICS AND COMPUTING

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San, Omer [Oklahoma State Univ., Stillwater, OK (United States)]. 2024-12-17. Physics-Reinforced Machine Learning Algorithms for Multiscale Closure Model Discovery. https://doi.org/10.2172/2481519

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