scholarly journals Adaptive Bi-objective Genetic Programming for Data-Driven System Modeling

2016 ◽  
pp. 248-259 ◽  
Author(s):  
Vitoantonio Bevilacqua ◽  
Nicola Nuzzolese ◽  
Ernesto Mininno ◽  
Giovanni Iacca
Keyword(s):  
Genetic Programming ◽  
System Modeling ◽  
Data Driven ◽  
Driven System

scholarly journals Dynamic Data Driven Methodologies for Multiphysics System Modeling and Simulation

2005 ◽  
pp. 616-623 ◽  
Author(s):  
J. Michopoulos ◽  
C. Farhat ◽  
E. Houstis ◽  
P. Tsompanopoulou ◽  
H. Zhang ◽  
...  
Keyword(s):  
Modeling And Simulation ◽  
System Modeling ◽  
Data Driven ◽  
Dynamic Data

Self-adjusted Data-Driven System for Prediction of Human Performance

2020 ◽  
pp. 282-287
Author(s):  
Oleksandr Burov ◽  
Evgeniy Lavrov ◽  
Nadiia Pasko ◽  
Olena Hlazunova ◽  
Olga Lavrova ◽  
...  
Keyword(s):  
Human Performance ◽  
Data Driven ◽  
Driven System

Design of a Dynamic Data-Driven System for Multispectral Video Processing

2018 ◽  
pp. 529-545 ◽  
Author(s):  
Honglei Li ◽  
Yanzhou Liu ◽  
Kishan Sudusinghe ◽  
Jinsung Yoon ◽  
Erik Blasch ◽  
...  
Keyword(s):  
Video Processing ◽  
Data Driven ◽  
Dynamic Data ◽  
Driven System

On the use of the data- and physics-driven approaches for quasi-geostrophic double-gyre problem: application of Genetic Programming

2021 ◽  
Author(s):  
Elnaz Naghibi ◽  
Elnaz Naghibi ◽  
Sergey Karabasov ◽  
Vassili Toropov ◽  
Vasily Gryazev
Keyword(s):  
Genetic Programming ◽  
Data Driven ◽  
Time Signal ◽  
Reduced Order ◽  
Multi Scale ◽  
Data Driven Approach ◽  
Auto Correlation Function ◽  
Gyre Circulation ◽  
Proper Orthogonal ◽  
Double Gyre

<p>In this study, we investigate Genetic Programming as a data-driven approach to reconstruct eddy-resolved simulations of the double-gyre problem. Stemming from Genetic Algorithms, Genetic Programming is a method of symbolic regression which can be used to extract temporal or spatial functionalities from simulation snapshots.  The double-gyre circulation is simulated by a stratified quasi-geostrophic model which is solved using high-resolution CABARET scheme. The simulation results are compressed using proper orthogonal decomposition and the time variant coefficients of the reduced-order model are fed into a Genetic Programming code. Due to the multi-scale nature of double-gyre problem, we decompose the time signal into a meandering and a fluctuating component. We next explore the parameter space of objective functions in Genetic Programming to capture the two components separately. The data-driven predictions are cross-compared with original double-gyre signal in terms of statistical moments such as variance and auto-correlation function.</p><p> </p>

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