Enhancing computational fluid dynamics with machine learning
Abstract Machine learning is rapidly becoming a core technology for scientific computing,
with numerous opportunities to advance the field of computational fluid dynamics. Here we …
with numerous opportunities to advance the field of computational fluid dynamics. Here we …
Turbulence modeling in the age of data
Data from experiments and direct simulations of turbulence have historically been used to
calibrate simple engineering models such as those based on the Reynolds-averaged Navier …
calibrate simple engineering models such as those based on the Reynolds-averaged Navier …
Super-resolution reconstruction of turbulent flows with machine learning
We use machine learning to perform super-resolution analysis of grossly under-resolved
turbulent flow field data to reconstruct the high-resolution flow field. Two machine learning …
turbulent flow field data to reconstruct the high-resolution flow field. Two machine learning …
Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework
Reynolds-averaged Navier-Stokes (RANS) equations are widely used in engineering
turbulent flow simulations. However, RANS predictions may have large discrepancies due to …
turbulent flow simulations. However, RANS predictions may have large discrepancies due to …
Solver-in-the-loop: Learning from differentiable physics to interact with iterative pde-solvers
Finding accurate solutions to partial differential equations (PDEs) is a crucial task in all
scientific and engineering disciplines. It has recently been shown that machine learning …
scientific and engineering disciplines. It has recently been shown that machine learning …
Unsupervised deep learning for super-resolution reconstruction of turbulence
Recent attempts to use deep learning for super-resolution reconstruction of turbulent flows
have used supervised learning, which requires paired data for training. This limitation …
have used supervised learning, which requires paired data for training. This limitation …
A point-cloud deep learning framework for prediction of fluid flow fields on irregular geometries
We present a novel deep learning framework for flow field predictions in irregular domains
when the solution is a function of the geometry of either the domain or objects inside the …
when the solution is a function of the geometry of either the domain or objects inside the …
Perspectives on machine learning-augmented Reynolds-averaged and large eddy simulation models of turbulence
K Duraisamy - Physical Review Fluids, 2021 - APS
This work presents a review and perspectives on recent developments in the use of machine
learning (ML) to augment Reynolds-averaged Navier-Stokes (RANS) and large eddy …
learning (ML) to augment Reynolds-averaged Navier-Stokes (RANS) and large eddy …
Data‐driven equation discovery of ocean mesoscale closures
The resolution of climate models is limited by computational cost. Therefore, we must rely on
parameterizations to represent processes occurring below the scale resolved by the models …
parameterizations to represent processes occurring below the scale resolved by the models …
A review of physics-informed machine learning in fluid mechanics
Physics-informed machine-learning (PIML) enables the integration of domain knowledge
with machine learning (ML) algorithms, which results in higher data efficiency and more …
with machine learning (ML) algorithms, which results in higher data efficiency and more …