Machine learning active-nematic hydrodynamics

DOI10.1073/PNAS.2016708118zbMath1485.76005OpenAlexW3036364291WikidataQ124987125 ScholiaQ124987125MaRDI QIDQ5073282

Jonathan Colen, Vincenzo Vitelli, Ming Han

Publication date: 5 May 2022

Published in: Proceedings of the National Academy of Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1073/pnas.2016708118

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Liquid crystals (76A15) Basic methods in fluid mechanics (76M99) Mathematical modeling or simulation for problems pertaining to fluid mechanics (76-10)

Related Items (1)

Vibration induced by active nematics

Cites Work

• Statistical hydrodynamics of ordered suspensions of self-propelled particles: Waves, giant number fluctuations and instabilities
• Lattice Boltzmann algorithm for three–dimensional liquid–crystal hydrodynamics
• Machine Learning for Fluid Mechanics
• Transition from turbulent to coherent flows in confined three-dimensional active fluids
• Dynamic response and hydrodynamics of polarized crowds
• Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
• Turbulence Modeling in the Age of Data

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