The most robust representations of flow trajectories are Lagrangian coherent structures
DOI10.1017/jfm.2021.768zbMath1481.76202OpenAlexW3202800914MaRDI QIDQ5157299
David H. Richter, Theodore MacMillan
Publication date: 13 October 2021
Published in: Journal of Fluid Mechanics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1017/jfm.2021.768
autoencoderpseudospectral methodneural networkpattern formationlow-dimensional modelturbulent Bickley jet
Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20) Spectral methods applied to problems in fluid mechanics (76M22) Turbulent transport, mixing (76F25) Diffusion and convection (76R99)
Cites Work
- Discovering governing equations from data by sparse identification of nonlinear dynamical systems
- Lagrangian coherent structures and mixing in two-dimensional turbulence
- Fast and robust computation of coherent Lagrangian vortices on very large two-dimensional domains
- Definition and properties of Lagrangian coherent structures from finite-time Lyapunov exponents in two-dimensional aperiodic flows
- Defining coherent vortices objectively from the vorticity
- Machine Learning for Fluid Mechanics
- A critical comparison of Lagrangian methods for coherent structure detection
- Coherent structure colouring: identification of coherent structures from sparse data using graph theory
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