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RotEqNet: Rotation-equivariant network for fluid systems with symmetric high-order tensors

Journal Article · · Journal of Computational Physics

Not provided.

Research Organization:
Purdue Univ., West Lafayette, IN (United States)
Sponsoring Organization:
USDOE Office of Science (SC)
DOE Contract Number:
SC0021142
OSTI ID:
1977283
Journal Information:
Journal of Computational Physics, Vol. 461, Issue C; ISSN 0021-9991
Publisher:
Elsevier
Country of Publication:
United States
Language:
English

References (20)

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Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty journal August 2015
Neural Network Modeling for Near Wall Turbulent Flow journal October 2002
Deep neural networks for data-driven LES closure models journal December 2019
Multilayer feedforward networks are universal approximators journal January 1989
Machine learning strategies for systems with invariance properties journal August 2016
Reynolds averaged turbulence modelling using deep neural networks with embedded invariance journal October 2016
A more general effective-viscosity hypothesis journal November 1975
On isotropic integrity bases journal January 1965
Learnability and the Vapnik-Chervonenkis dimension journal October 1989
A theory of the learnable journal November 1984
Subgrid-scale modelling for the large-eddy simulation of high-Reynolds-number boundary layers journal April 1997
Large-Eddy Simulation of Turbulent Combustion journal January 2006
A deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials journal March 2019
A general regression neural network journal January 1991

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