Machine Learning Applications in Convective Turbulence

Autor: Kräuter, Robert, Krasnov, Dmitry, Pandey, Ambrish, Schneide, Christiane, Padberg-Gehle, Kathrin, Giannakis, Dimitrios, Sreenivasan, Katepelli R., Schumacher, Jörg

Publikováno v: Jülich : Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag, Publication Series of the John von Neumann Institute for Computing (NIC) NIC Series 50, 357-366 (2020).
NIC Symposium 2020
NIC Symposium 2020NIC Symposium 2020, Jülich, Germany, 2020-02-27-2020-02-28
Kräuter, R, Krasnov, D, Pandey, A, Schneide, C, Padberg-Gehle, K, Giannakis, D, Sreenivasan, K R & Schumacher, J 2020, Machine Learning Applications in Convective Turbulence . in M Müller, K Binder & A Trauntmann (eds), NIC Symposium 2020 : 27 − 28 February 2020, Jülich, Germany, Proceedings . Publication Series of the John von Neumann Institute for Computing (NIC), vol. 50, Forschungszentrum Jülich, Jülich, pp. 357-366, 10th John von Neumann Institute for Computing Symposium-2020, Jülich, Germany, 27.02.20 . < http://juser.fz-juelich.de/record/874547?ln=de >

Turbulent convection flows are ubiquitous in natural systems such as in the atmosphere or in stellar interiors as well as in technological applications such as cooling or energy storage devices. Their physical complexity and vast number of degrees of

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https://juser.fz-juelich.de/record/874547