A Predictive Data-Driven Approach Based on Reduced Order Models for the Morphodynamic Study of a Coastal Water Intake

2020 ◽  
pp. 849-866
Author(s):  
Rem-Sophia Mouradi ◽  
Cédric Goeury ◽  
Olivier Thual ◽  
Fabrice Zaoui ◽  
Pablo Tassi
Keyword(s):  
Coastal Water ◽  
Water Intake ◽  
Data Driven ◽  
Reduced Order Models ◽  
Reduced Order ◽  
Data Driven Approach

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>

scholarly journals Data-driven variational multiscale reduced order models

2021 ◽  
Vol 373 ◽  
pp. 113470 ◽  
Author(s):  
Changhong Mou ◽  
Birgul Koc ◽  
Omer San ◽  
Leo G. Rebholz ◽  
Traian Iliescu
Keyword(s):  
Data Driven ◽  
Reduced Order Models ◽  
Reduced Order ◽  
Variational Multiscale

scholarly journals Data-driven reduced-order models for rank-ordering the high cycle fatigue performance of polycrystalline microstructures

2018 ◽  
Vol 154 ◽  
pp. 170-183 ◽  
Author(s):  
Noah H. Paulson ◽  
Matthew W. Priddy ◽  
David L. McDowell ◽  
Surya R. Kalidindi
Keyword(s):  
High Cycle Fatigue ◽  
Fatigue Performance ◽  
Data Driven ◽  
Reduced Order Models ◽  
Cycle Fatigue ◽  
Reduced Order ◽  
Rank Ordering
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