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Forecasting subcritical cylinder wakes with Fourier Neural Operators

Renn, Peter I and Wang, Cong and Lale, Sahin and Li, Zongyi and Anandkumar, Anima and Gharib, Morteza (2023) Forecasting subcritical cylinder wakes with Fourier Neural Operators. . (Unpublished)

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We apply Fourier neural operators (FNOs), a state-of-the-art operator learning technique, to forecast the temporal evolution of experimentally measured velocity fields. FNOs are a recently developed machine learning method capable of approximating solution operators to systems of partial differential equations through data alone. The learned FNO solution operator can be evaluated in milliseconds, potentially enabling faster-than-real-time modeling for predictive flow control in physical systems. Here we use FNOs to predict how physical fluid flows evolve in time, training with particle image velocimetry measurements depicting cylinder wakes in the subcritical vortex shedding regime. We train separate FNOs at Reynolds numbers ranging from Re = 240 to Re = 3060 and study how increasingly turbulent flow phenomena impact prediction accuracy. We focus here on a short prediction horizon of ten non-dimensionalized time-steps, as would be relevant for problems of predictive flow control. We find that FNOs are capable of accurately predicting the evolution of experimental velocity fields throughout the range of Reynolds numbers tested (L2 norm error < 0.1) despite being provided with limited and imperfect flow observations. Given these results, we conclude that this method holds significant potential for real-time predictive flow control of physical systems.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper
Renn, Peter I0000-0002-5735-3873
Wang, Cong0000-0002-8271-5637
Lale, Sahin0000-0002-7191-346X
Li, Zongyi0000-0003-2081-9665
Anandkumar, Anima0000-0002-6974-6797
Gharib, Morteza0000-0003-0754-4193
Additional Information:This work was supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1745301, Bren endowed chair, Kortschak Scholars, PIMCO Fellows, Amazon AI4Science Fellows, and the Center for Autonomous Systems and Technologies at Caltech.
Group:GALCIT, Center for Autonomous Systems and Technologies (CAST)
Funding AgencyGrant Number
NSF Graduate Research FellowshipDGE-1745301
Bren Professor of Computing and Mathematical SciencesUNSPECIFIED
Kortschak Scholars ProgramUNSPECIFIED
Amazon AI4Science FellowshipUNSPECIFIED
Center for Autonomous Systems and TechnologiesUNSPECIFIED
Record Number:CaltechAUTHORS:20230316-153752294
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:120088
Deposited By: George Porter
Deposited On:16 Mar 2023 19:28
Last Modified:16 Mar 2023 19:28

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