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 …
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 …
Explainable machine learning for scientific insights and discoveries
Machine learning methods have been remarkably successful for a wide range of application
areas in the extraction of essential information from data. An exciting and relatively recent …
areas in the extraction of essential information from data. An exciting and relatively recent …
Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
Numerical simulations on fluid dynamics problems primarily rely on spatially or/and
temporally discretization of the governing equation using polynomials into a finite …
temporally discretization of the governing equation using polynomials into a finite …
Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
There exists significant demand for improved Reynolds-averaged Navier–Stokes (RANS)
turbulence models that are informed by and can represent a richer set of turbulence physics …
turbulence models that are informed by and can represent a richer set of turbulence physics …
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 …
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 …
Neural networks meet hyperelasticity: A guide to enforcing physics
In the present work, a hyperelastic constitutive model based on neural networks is proposed
which fulfills all common constitutive conditions by construction, and in particular, is …
which fulfills all common constitutive conditions by construction, and in particular, is …
Driven by data or derived through physics? a review of hybrid physics guided machine learning techniques with cyber-physical system (cps) focus
A multitude of cyber-physical system (CPS) applications, including design, control,
diagnosis, prognostics, and a host of other problems, are predicated on the assumption of …
diagnosis, prognostics, and a host of other problems, are predicated on the assumption of …
Quantification of model uncertainty in RANS simulations: A review
H Xiao, P Cinnella - Progress in Aerospace Sciences, 2019 - Elsevier
In computational fluid dynamics simulations of industrial flows, models based on the
Reynolds-averaged Navier–Stokes (RANS) equations are expected to play an important …
Reynolds-averaged Navier–Stokes (RANS) equations are expected to play an important …