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Predicting Full-Field Turbulent Flows Using Fourier Neural Operator
Peter Renn · Sahin Lale · Cong Wang · Zongyi Li · Anima Anandkumar · Morteza Gharib
We present an experimental application of Fourier neural operators (FNOs) for predicting the temporal development of wakes behind tandem bluff body arrangements at a Reynolds number of $Re \approx 1500$. FNOs are recently introduced tools in machine learning capable of approximating solution operators to partial differential equations, such as the Navier-Stokes equations, through data alone. Once trained, FNOs can predict full-field solutions in milliseconds. Here we apply this method to experimental velocity fields acquired via particle image velocimetry and compare the predicted temporal developments of the learned solution operator with the actual measurements taken at those timesteps. We find that FNOs are capable of accurately predicting wake developments hundreds of milliseconds into the future. Using several tandem cylinder configurations, we also demonstrate that learned solution operators are surprisingly capable of adapting to unseen conditions and generalizing wake dynamics across different arrangements.

Author Information

Peter Renn (California Institute of Technology)
Sahin Lale (California Institute of Technology)
Cong Wang (California Institute of Technology)
Zongyi Li (Caltech)
Anima Anandkumar (NVIDIA / Caltech)

Anima Anandkumar is a Bren professor at Caltech CMS department and a director of machine learning research at NVIDIA. Her research spans both theoretical and practical aspects of large-scale machine learning. In particular, she has spearheaded research in tensor-algebraic methods, non-convex optimization, probabilistic models and deep learning. Anima is the recipient of several awards and honors such as the Bren named chair professorship at Caltech, Alfred. P. Sloan Fellowship, Young investigator awards from the Air Force and Army research offices, Faculty fellowships from Microsoft, Google and Adobe, and several best paper awards. Anima received her B.Tech in Electrical Engineering from IIT Madras in 2004 and her PhD from Cornell University in 2009. She was a postdoctoral researcher at MIT from 2009 to 2010, a visiting researcher at Microsoft Research New England in 2012 and 2014, an assistant professor at U.C. Irvine between 2010 and 2016, an associate professor at U.C. Irvine between 2016 and 2017 and a principal scientist at Amazon Web Services between 2016 and 2018.

Morteza Gharib (California Institute of Technology)

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