Modal extraction of spatiotemporal atomization data using a deep convolutional Koopman network
Author:
Affiliation:
1. Department of Mechanical and Aerospace Engineering, University of California, Irvine, California, 92697, USA
Publisher
AIP Publishing
Subject
Condensed Matter Physics,Fluid Flow and Transfer Processes,Mechanics of Materials,Computational Mechanics,Mechanical Engineering
Link
https://aip.scitation.org/doi/pdf/10.1063/5.0046177
Reference47 articles.
1. Spectral Properties of Dynamical Systems, Model Reduction and Decompositions
2. A Data–Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition
3. On Convergence of Extended Dynamic Mode Decomposition to the Koopman Operator
4. Dynamic mode decomposition of numerical and experimental data
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