scholarly journals Multiscale modeling high-order methods and data-driven modeling.

2020 ◽  
Author(s):  
Eric Parish
Keyword(s):  
Multiscale Modeling ◽  
High Order ◽  
Data Driven ◽  
High Order Methods ◽  
Data Driven Modeling

High order boundary treatment for high order methods in computational fluid dynamics

2016 ◽  
Author(s):  
André Ribeiro de Barros Aguiar ◽  
Carlos Breviglieri ◽  
Fábio Mallaco Moreira ◽  
Eduardo Jourdan ◽  
João Luiz F. Azevedo
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
High Order ◽  
High Order Methods ◽  
Boundary Treatment

High-Order Methods For Wave Propagation

2008 ◽  
Author(s):  
Miguel R. Visbal ◽  
Scott E. Sherer ◽  
Michael D. White
Keyword(s):  
Wave Propagation ◽  
High Order ◽  
High Order Methods

scholarly journals Are college campuses superspreaders? A data-driven modeling study

2021 ◽  
pp. 1-11
Author(s):  
Hannah Lu ◽  
Cortney Weintz ◽  
Joseph Pace ◽  
Dhiraj Indana ◽  
Kevin Linka ◽  
...  
Keyword(s):  
College Campuses ◽  
Data Driven ◽  
Data Driven Modeling ◽  
Modeling Study

scholarly journals Gain-Preserving Data-Driven Approximation of the Koopman Operator and Its Application in Robust Controller Design

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 949
Author(s):  
Keita Hara ◽  
Masaki Inoue
Keyword(s):  
Controller Design ◽  
Internal Model Control ◽  
Feedback Controller ◽  
Data Driven ◽  
Koopman Operator ◽  
Plant System ◽  
Dimensional Approximation ◽  
Model Control ◽  
Data Driven Modeling ◽  
L2 Gain

In this paper, we address the data-driven modeling of a nonlinear dynamical system while incorporating a priori information. The nonlinear system is described using the Koopman operator, which is a linear operator defined on a lifted infinite-dimensional state-space. Assuming that the L2 gain of the system is known, the data-driven finite-dimensional approximation of the operator while preserving information about the gain, namely L2 gain-preserving data-driven modeling, is formulated. Then, its computationally efficient solution method is presented. An application of the modeling method to feedback controller design is also presented. Aiming for robust stabilization using data-driven control under a poor training dataset, we address the following two modeling problems: (1) Forward modeling: the data-driven modeling is applied to the operating data of a plant system to derive the plant model; (2) Backward modeling: L2 gain-preserving data-driven modeling is applied to the same data to derive an inverse model of the plant system. Then, a feedback controller composed of the plant and inverse models is created based on internal model control, and it robustly stabilizes the plant system. A design demonstration of the data-driven controller is provided using a numerical experiment.

Data-Driven Modeling Approach to Predict the Recovery Performance of Low-Salinity Waterfloods

2021 ◽  
Author(s):  
Shams Kalam ◽  
Rizwan Ahmed Khan ◽  
Shahnawaz Khan ◽  
Muhammad Faizan ◽  
Muhammad Amin ◽  
...  
Keyword(s):  
Data Driven ◽  
Low Salinity ◽  
Modeling Approach ◽  
Recovery Performance ◽  
Data Driven Modeling

scholarly journals From SIR to SEAIRD: A novel data-driven modeling approach based on the Grey-box System Theory to predict the dynamics of COVID-19

2021 ◽  
Author(s):  
K. Midzodzi Pekpe ◽  
Djamel Zitouni ◽  
Gilles Gasso ◽  
Wajdi Dhifli ◽  
Benjamin C. Guinhouya
Keyword(s):  
System Theory ◽  
Data Driven ◽  
Modeling Approach ◽  
Data Driven Modeling
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