Reduced order modeling of fluid flows: Machine learning, Kolmogorov barrier, closure modeling, and partitioning (Invited)
Author:
Affiliation:
1. Oklahoma State University
2. Norwegian University of Science and Technology
Publisher
American Institute of Aeronautics and Astronautics
Reference126 articles.
1. Krylov subspace techniques for reduced-order modeling of large-scale dynamical systems
2. Reduced-order modeling: new approaches for computational physics
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4. Nonlinear Model Order Reduction via Lifting Transformations and Proper Orthogonal Decomposition
5. Projection-based model reduction: Formulations for physics-based machine learning
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2. A surrogate non-intrusive reduced order model of quasi-geostrophic turbulence dynamics based on a combination of LSTM and different approaches of DMD;Journal of Turbulence;2023-10-03
3. Physics Guided Machine Learning for Variational Multiscale Reduced Order Modeling;SIAM Journal on Scientific Computing;2023-06-06
4. Three-dimensional deep learning-based reduced order model for unsteady flow dynamics with variable Reynolds number;Physics of Fluids;2022-03
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