scholarly journals A ferroelectric and ferroelastic microscopic switching criterion for tetragonal ferroelectrics

PAMM ◽  
2011 ◽  
Vol 11 (1) ◽  
pp. 475-476 ◽  
Author(s):  
Marc-André Keip ◽  
Jörg Schröder
Keyword(s):  
Switching Criterion

scholarly journals A kinetic switching criterion for hybrid modelling of multiscale gas flows

2012 ◽  
Vol 362 ◽  
pp. 012006 ◽  
Author(s):  
Jianping Meng ◽  
Nishanth Dongari ◽  
Jason M Reese ◽  
Yonghao Zhang
Keyword(s):  
Gas Flows ◽  
Hybrid Modelling ◽  
Switching Criterion

Design of Hybrid Reconstruction Scheme for Compressible Flow Using Data-Driven Methods

2020 ◽  
Vol 36 (5) ◽  
pp. 675-689
Author(s):  
A. Salazar ◽  
F. Xiao
Keyword(s):  
Machine Learning ◽  
Compressible Flow ◽  
Machine Learning Techniques ◽  
Hybrid Scheme ◽  
Solution Quality ◽  
Hybrid Schemes ◽  
New Type ◽  
Switching Criterion ◽  
Using Data ◽  
Discontinuous Data

ABSTRACTExisting numerical schemes used to solve the governing equations for compressible flow suffer from dissipation errors which tend to smear out sharp discontinuities. Hybrid schemes show potential improvements in this challenging problem; however, the solution quality of a hybrid scheme heavily depends on the criterion to switch between the different candidate reconstruction functions. This work presents a new type of switching criterion (or selector) using machine learning techniques. The selector is trained with randomly generated samples of continuous and discontinuous data profiles, using the exact solution of the governing equation as a reference. Neural networks and random forests were used as the machine learning frameworks to train the selector, and it was later implemented as the indicator function in a hybrid scheme which includes THINC and WENO-Z as the candidate reconstruction functions. The trained selector has been verified to be effective as a reliable switching criterion in the hybrid scheme, which significantly improves the solution quality for both advection and Euler equations.

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