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SUMMARY:Synchronization in Navier-Stokes turbulence and its role in data-d
 riven modeling - Professor Masanobu Inubushi\, Tokyo University of Science
DTSTART:20250214T160000Z
DTEND:20250214T170000Z
UID:TALK226264@talks.cam.ac.uk
CONTACT:Professor Grae Worster
DESCRIPTION:In Navier-Stokes (NS) turbulence\, large-scale turbulent flows
  determine small-scale flows\; in other words\, small-scale flows are sync
 hronized to large-scale flows. In 3D turbulence\, previous numerical studi
 es suggest that the critical length separating these two scales is determi
 ned by the Kolmogorov length. In this talk\, I will introduce our theoreti
 cal framework for characterizing synchronization phenomena [1]. Specifical
 ly\, it provides a computational method for the exponential rate of conver
 gence to the synchronized state\, and identifies the critical length based
  on the NS equations via the "transverse" Lyapunov exponent. I will also d
 iscuss the synchronization property of 2D NS turbulence and how it differs
  from the 3D case [2]. These insights into synchronization and critical le
 ngth scales are essential for developing machine-learning closure models f
 or turbulence\, in particular their stable reproducibility [3]. Finally\, 
 I will illustrate how "generalized" synchronization is crucial for predict
 ing chaotic dynamics [4].\n\n[1] M. Inubushi\, Y. Saiki\, M. U. Kobayashi\
 , and S. Goto\, Characterizing small-scale dynamics of Navier-Stokes turbu
 lence with transverse Lyapunov exponents: A data assimilation approach\, P
 hys. Rev. Lett. 131\, 254001 (2023).\n\n[2] M. Inubushi and C. P. Caulfiel
 d (in preparation).\n\n[3] S. Matsumoto\, M. Inubushi\, and S. Goto\, Stab
 le reproducibility of turbulence dynamics by machine learning\, Phys. Rev.
  Fluids 9\, 104601 (2024).\n\n[4] A. Ohkubo and M. Inubushi\, Reservoir co
 mputing with generalized readout based on generalized synchronization\, Sc
 i. Rep. 14\, 30918 (2024).\n\n\n\n
LOCATION:MR2
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