Optimization Methods for Dynamic Mode Decomposition of Nonlinear Partial Differential Equations
| dc.contributor.author | Zigic, Jovan | en |
| dc.contributor.committeechair | Borggaard, Jeffrey T. | en |
| dc.contributor.committeemember | Zietsman, Lizette | en |
| dc.contributor.committeemember | Lin, Tao | en |
| dc.contributor.department | Mathematics | en |
| dc.date.accessioned | 2021-06-15T08:01:06Z | en |
| dc.date.available | 2021-06-15T08:01:06Z | en |
| dc.date.issued | 2021-06-14 | en |
| dc.description.abstract | Reduced-order models have long been used to understand the behavior of nonlinear partial differential equations. Naturally, reduced-order modeling techniques come at the price of either computational accuracy or computation time. Optimization techniques are studied to improve either or both of these objectives and decrease the total computational cost of the problem. This thesis focuses on the dynamic mode decomposition (DMD) applied to nonlinear PDEs with periodic boundary conditions. It provides one study of an existing optimization framework for the DMD method known as the Optimized DMD and provides another study of a newly proposed optimization framework for the DMD method called the Split DMD. | en |
| dc.description.abstractgeneral | The Navier-Stokes (NS) equations are the primary mathematical model for understanding the behavior of fluids. The existence and smoothness of the NS equations is considered to be one of the most important open problems in mathematics, and challenges in their numerical simulation is a barrier to understanding the physical phenomenon of turbulence. Due to the difficulty of studying this problem directly, simpler problems in the form of nonlinear partial differential equations (PDEs) that exhibit similar properties to the NS equations are studied as preliminary steps towards building a wider understanding of the field. Reduced-order models have long been used to understand the behavior of nonlinear partial differential equations. Naturally, reduced-order modeling techniques come at the price of either computational accuracy or computation time. Optimization techniques are studied to improve either or both of these objectives and decrease the total computational cost of the problem. This thesis focuses on the dynamic mode decomposition (DMD) applied to nonlinear PDEs with periodic boundary conditions. It provides one study of an existing optimization framework for the DMD method known as the Optimized DMD and provides another study of a newly proposed optimization framework for the DMD method called the Split DMD. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:31161 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/103862 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Optimization | en |
| dc.subject | Model Order Reduction | en |
| dc.subject | Partial Differential Equations | en |
| dc.title | Optimization Methods for Dynamic Mode Decomposition of Nonlinear Partial Differential Equations | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Mathematics | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |
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