scholarly journals Physics‐Informed Deep Neural Networks for Learning Parameters and Constitutive Relationships in Subsurface Flow Problems

2020 ◽  
Vol 56 (5) ◽  
Author(s):  
A. M. Tartakovsky ◽  
C. Ortiz Marrero ◽  
Paris Perdikaris ◽  
G. D. Tartakovsky ◽  
D. Barajas‐Solano
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Subsurface Flow ◽  
Flow Problems ◽  
Constitutive Relationships

scholarly journals Deep learning or interpolation for inverse modelling of heat and fluid flow problems?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rainald Löhner ◽  
Harbir Antil ◽  
Hamid Tamaddon-Jahromi ◽  
Neeraj Kavan Chakshu ◽  
Perumal Nithiarasu
Keyword(s):  
Neural Networks ◽  
Heat Conduction ◽  
Deep Neural Networks ◽  
Nonlinear Behaviour ◽  
Content Type ◽  
Flow Problems ◽  
Interpolation Algorithms ◽  
Transfer Problems ◽  
First Time ◽  
Heat And Fluid Flow

Purpose The purpose of this study is to compare interpolation algorithms and deep neural networks for inverse transfer problems with linear and nonlinear behaviour. Design/methodology/approach A series of runs were conducted for a canonical test problem. These were used as databases or “learning sets” for both interpolation algorithms and deep neural networks. A second set of runs was conducted to test the prediction accuracy of both approaches. Findings The results indicate that interpolation algorithms outperform deep neural networks in accuracy for linear heat conduction, while the reverse is true for nonlinear heat conduction problems. For heat convection problems, both methods offer similar levels of accuracy. Originality/value This is the first time such a comparison has been made.

Deep neural networks trained with heavier data augmentation learn features closer to representations in hIT

2018 ◽  
Author(s):  
Alex Hernández-García ◽  
Johannes Mehrer ◽  
Nikolaus Kriegeskorte ◽  
Peter König ◽  
Tim C. Kietzmann
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Data Augmentation

Representation of adversarial images in deep neural networks and the human brain

2018 ◽  
Author(s):  
Chi Zhang ◽  
Xiaohan Duan ◽  
Ruyuan Zhang ◽  
Li Tong
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Deep Neural Networks
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