Reduced-Order Modeling for Dynamic Mode Decomposition Without an Arbitrary Sparsity Parameter
Author:
Affiliation:
1. University at Buffalo, The State University of New York, Buffalo, New York 14260
Publisher
American Institute of Aeronautics and Astronautics (AIAA)
Subject
Aerospace Engineering
Reference34 articles.
1. Dynamic mode decomposition of numerical and experimental data
2. Discovering governing equations from data by sparse identification of nonlinear dynamical systems
3. Deep learning of vortex-induced vibrations
4. Linearly Recurrent Autoencoder Networks for Learning Dynamics
5. Sparse Identification of Nonlinear Dynamics with Control (SINDYc)**SLB acknowledges support from the U.S. Air Force Center of Excellence on Nature Inspired Flight Technologies and Ideas (FA9550-14-1-0398). JLP thanks Bill and Melinda Gates for their active support of the Institute of Disease Modeling and their sponsorship through the Global Good Fund. JNK acknowledges support from the U.S. Air Force Office of Scientific Research (FA9550-09-0174).
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