Physics Guided Machine Learning for Variational Multiscale Reduced Order Modeling-Reference-Cited by-同舟云学术

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Physics Guided Machine Learning for Variational Multiscale Reduced Order Modeling

Published:2023-06-06 Issue:3 Volume:45 Page:B283-B313
ISSN:1064-8275
Container-title:SIAM Journal on Scientific Computing
language:en
Short-container-title:SIAM J. Sci. Comput.

Author:

Ahmed Shady E.¹^ORCID,San Omer¹^ORCID,Rasheed Adil²,Iliescu Traian³,Veneziani Alessandro⁴^ORCID

Affiliation:

1. School of Mechanical & Aerospace Engineering, Oklahoma State University, Stillwater, OK 74078 USA.

2. Department of Engineering Cybernetics, Norwegian University of Science and Technology, N-7465, Trondheim, Norway.

3. Department of Mathematics, Virginia Tech, Blacksburg, VA 24061 USA.

4. Department of Mathematics, Department of Computer Science, Emory University, Atlanta, GA 30322 USA.

Funder

National Science Foundation

U.S. Department of Energy

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

Applied Mathematics,Computational Mathematics

Reference87 articles.

1. Reduced order modeling of fluid flows: Machine learning, Kolmogorov barrier, closure modeling, and partitioning (Invited)

2. On closures for reduced order models—A spectrum of first-principle to machine-learned avenues

3. Breaking the Kolmogorov Barrier in Model Reduction of Fluid Flows

4. A long short-term memory embedding for hybrid uplifted reduced order models

5. Nonlinear proper orthogonal decomposition for convection-dominated flows

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Switching diffusions for multiscale uncertainty quantification;International Journal of Non-Linear Mechanics;2024-10

2. Bridging Large Eddy Simulation and Reduced-Order Modeling of Convection-Dominated Flows through Spatial Filtering: Review and Perspectives;Fluids;2024-08-04

3. A new hybrid reduced order modeling for parametrized Navier–Stokes equations in stream-vorticity formulation;Physics of Fluids;2024-06-01

4. Reduced Basis modelling of turbulence with well-developed inertial range;Computer Methods in Applied Mechanics and Engineering;2024-02

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