Machine Learning Applications in Convective Turbulence

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearch

Standard

Machine Learning Applications in Convective Turbulence. / Kräuter, Robert; Krasnov, Dmitry; Pandey, Ambrish et al.
NIC Symposium 2020: 27 − 28 February 2020, Jülich, Germany, Proceedings. ed. / M. Müller; K. Binder; A. Trauntmann. Jülich: Forschungszentrum Jülich , 2020. p. 357-366 (Publication Series of the John von Neumann Institute for Computing (NIC); Vol. 50).

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearch

Harvard

Kräuter, R, Krasnov, D, Pandey, A, Schneide, C, Padberg-Gehle, K, Giannakis, D, Sreenivasan, KR & 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>

APA

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 (pp. 357-366). (Publication Series of the John von Neumann Institute for Computing (NIC); Vol. 50). Forschungszentrum Jülich . http://juser.fz-juelich.de/record/874547?ln=de

Vancouver

Kräuter R, Krasnov D, Pandey A, Schneide C, Padberg-Gehle K, Giannakis D et al. Machine Learning Applications in Convective Turbulence. In Müller M, Binder K, Trauntmann A, editors, NIC Symposium 2020: 27 − 28 February 2020, Jülich, Germany, Proceedings. Jülich: Forschungszentrum Jülich . 2020. p. 357-366. (Publication Series of the John von Neumann Institute for Computing (NIC)).

Bibtex

@inbook{e9bc21a504dc4844adf63cee48f1c434,
title = "Machine Learning Applications in Convective Turbulence",
abstract = "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 freedom prevents often an access by direct numerical simulations that resolve all flow scales from the smallest to the largest plumes and vortices in the system and requires a simplified modelling of the flow itself and the resulting turbulent transport behaviour. The following article summarises some examples that aim at a reduction of the flow complexity and thus of the number of degrees of freedom of convective turbulence by machine learning approaches. We therefore apply unsupervised and supervised machine learning methods to direct numerical simulation data of a Rayleigh-B{\'e}nard convection flow which serves as a paradigm of the examples mentioned at the beginning.",
keywords = "Mathematics",
author = "Robert Kr{\"a}uter and Dmitry Krasnov and Ambrish Pandey and Christiane Schneide and Kathrin Padberg-Gehle and Dimitrios Giannakis and Sreenivasan, {Katepelli R.} and J{\"o}rg Schumacher",
year = "2020",
language = "English",
isbn = "978-3-95806-443-0 ",
series = "Publication Series of the John von Neumann Institute for Computing (NIC)",
publisher = "Forschungszentrum J{\"u}lich ",
pages = "357--366",
editor = "M. M{\"u}ller and K. Binder and A. Trauntmann",
booktitle = "NIC Symposium 2020",
note = "10th John von Neumann Institute for Computing Symposium - 2020, 10th NIC Symposium - 2020 ; Conference date: 27-02-2020 Through 28-02-2020",
url = "https://www.fz-juelich.de/SharedDocs/Termine/IAS/JSC/DE/Events/2020/nic-symposium-2020.html",

}

RIS

TY - CHAP

T1 - Machine Learning Applications in Convective Turbulence

AU - Kräuter, Robert

AU - Krasnov, Dmitry

AU - Pandey, Ambrish

AU - Schneide, Christiane

AU - Padberg-Gehle, Kathrin

AU - Giannakis, Dimitrios

AU - Sreenivasan, Katepelli R.

AU - Schumacher, Jörg

N1 - Conference code: 10

PY - 2020

Y1 - 2020

N2 - 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 freedom prevents often an access by direct numerical simulations that resolve all flow scales from the smallest to the largest plumes and vortices in the system and requires a simplified modelling of the flow itself and the resulting turbulent transport behaviour. The following article summarises some examples that aim at a reduction of the flow complexity and thus of the number of degrees of freedom of convective turbulence by machine learning approaches. We therefore apply unsupervised and supervised machine learning methods to direct numerical simulation data of a Rayleigh-Bénard convection flow which serves as a paradigm of the examples mentioned at the beginning.

AB - 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 freedom prevents often an access by direct numerical simulations that resolve all flow scales from the smallest to the largest plumes and vortices in the system and requires a simplified modelling of the flow itself and the resulting turbulent transport behaviour. The following article summarises some examples that aim at a reduction of the flow complexity and thus of the number of degrees of freedom of convective turbulence by machine learning approaches. We therefore apply unsupervised and supervised machine learning methods to direct numerical simulation data of a Rayleigh-Bénard convection flow which serves as a paradigm of the examples mentioned at the beginning.

KW - Mathematics

UR - http://juser.fz-juelich.de/record/874547?ln=de

UR - https://juser.fz-juelich.de/record/874262/files/NIC_Series_50.pdf

M3 - Article in conference proceedings

SN - 978-3-95806-443-0

T3 - Publication Series of the John von Neumann Institute for Computing (NIC)

SP - 357

EP - 366

BT - NIC Symposium 2020

A2 - Müller, M.

A2 - Binder, K.

A2 - Trauntmann, A.

PB - Forschungszentrum Jülich

CY - Jülich

T2 - 10th John von Neumann Institute for Computing Symposium - 2020

Y2 - 27 February 2020 through 28 February 2020

ER -