A Surrogate Modeling for Storm Surge Prediction Using an Artificial Neural Network

2018 ◽  
Vol 85 ◽  
pp. 866-870 ◽  
Author(s):  
Seung-Woo Kim ◽  
Anzy Lee ◽  
Jongyoon Mun
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Storm Surge ◽  
Surrogate Modeling ◽  
Artificial Neural

Artificial neural network-based surrogate modeling of multi-component dynamic adsorption of heavy metals with a biochar

2018 ◽  
Vol 6 (4) ◽  
pp. 5389-5400 ◽  
Author(s):  
J. Moreno-Pérez ◽  
A. Bonilla-Petriciolet ◽  
D.I. Mendoza-Castillo ◽  
H.E. Reynel-Ávila ◽  
Y. Verde-Gómez ◽  
...  
Keyword(s):  
Neural Network ◽  
Heavy Metals ◽  
Artificial Neural Network ◽  
Surrogate Modeling ◽  
Dynamic Adsorption ◽  
Artificial Neural

A review of the artificial neural network surrogate modeling in aerodynamic design

2019 ◽  
Vol 233 (16) ◽  
pp. 5863-5872 ◽  
Author(s):  
Gang Sun ◽  
Shuyue Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Design Space ◽  
Optimal Solution ◽  
Surrogate Modeling ◽  
Aerodynamic Design ◽  
Hidden Information ◽  
Data Manipulation ◽  
Artificial Neural ◽  
Selection Of

Artificial neural network surrogate modeling with its economic computational consumption and accurate generalization capabilities offers a feasible approach to aerodynamic design in the field of rapid investigation of design space and optimal solution searching. This paper reviews the basic principle of artificial neural network surrogate modeling in terms of data treatment and configuration setup. A discussion of artificial neural network surrogate modeling is held on different objectives in aerodynamic design applications, various patterns of realization via cutting-edge data technique in numerous optimizations, selection of network topology and types, and other measures for improving modeling. Then, new frontiers of modern artificial neural network surrogate modeling are reviewed with regard to exploiting the hidden information for bringing new perspectives to optimization by exploring new data form and patterns, e.g. quick provision of candidates of better aerodynamic performance via accumulated database instead of random seeding, and envisions of more physical understanding being injected to the data manipulation.

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