Learning Turbulence Models from Data: A Unified Perspective of Data Assimilation and Machine Learning
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Although turbulence affects natural and engineered systems from sub-meter to planetary scales, fundamental understanding and predictive modeling of turbulence continue to defy and bedevil scientists and engineers. This talk summarizes our work during the past decade in leveraging disparate data (ranging from sparse observations to full-field data) to enhance Reynolds-averaged Navier-Stokes (RANS) turbulence models in a physics-informed framework. Specifically, I will present (1) using sparse data to infer Reynolds stress fields based on ensemble data assimilation for reducing RANS model uncertainties, and (2) our ongoing work in unifying data assimilation and neural networks for parallel learning of turbulence models with quantified uncertainties.
