Dynamic calibration of differential equations using machine learning, with application to turbulence models
From MaRDI portal
Publication:2135788
DOI10.1016/j.jcp.2021.110924OpenAlexW4205216603WikidataQ115350053 ScholiaQ115350053MaRDI QIDQ2135788
I. Boureima, J. A. Saenz, Vitaliy Gyrya, Susan Kurien, Marianne M. Francois
Publication date: 9 May 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jcp.2021.110924
Related Items
Uses Software
Cites Work
- Unnamed Item
- Variable density fluid turbulence.
- Rayleigh-Taylor and Richtmyer-Meshkov instability induced flow, turbulence, and mixing. I
- Rayleigh-Taylor and Richtmyer-Meshkov instability induced flow, turbulence, and mixing. II
- Data-driven deep learning of partial differential equations in modal space
- Numerical aspects for approximating governing equations using data
- Data driven governing equations approximation using deep neural networks
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Application of a second-moment closure model to mixing processes involving multicomponent miscible fluids
- Turbulence with Large Thermal and Compositional Density Variations
- Variable-density mixing in buoyancy-driven turbulence
- A comparative study of the turbulent Rayleigh–Taylor instability using high-resolution three-dimensional numerical simulations: The Alpha-Group collaboration
- Effects of Atwood and Reynolds numbers on the evolution of buoyancy-driven homogeneous variable-density turbulence
- Buoyancy-driven variable-density turbulence
- Turbulent mixing in spherical implosions
- A numerical study of the influence of initial perturbations on the turbulent Rayleigh–Taylor instability
- The instability of liquid surfaces when accelerated in a direction perpendicular to their planes. I
