Articles Citing

School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, USA and Google Research, Mountain View, California 94103, USA

Mechanical and Aerospace Engineering, University of California–Los Angeles, Los Angeles, California 90095, USA

Mechanical Science & Engineering and Aerospace Engineering, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801, USA

^*mpbrenner@gmail.com
^†jdeldre@ucla.edu
^‡jbfreund@illinois.edu

Control of flow behavior in complex fluids using automatic differentiation

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Articles Citing

Perspective on machine learning for advancing fluid mechanics

Control of flow behavior in complex fluids using automatic differentiation

Vortex-induced drag forecast for cylinder in non-uniform inflow

Open-loop linear modeling method for unstable flow utilizing built-in data-driven feedback controllers

Force decomposition of vortex–plate interaction via dynamic mode decomposition

Dynamics of magnetized hybrid solid particles in a rotating cone with gradient descent optimization

Data-driven modeling of transonic nonlinear flutter via convolutional neural network autoencoder

FPGA-accelerated Binary Neural Networks as a surrogate model for a viscoplastic constitutive law in impulsively loaded beams

Image-based Artificial Intelligence-driven modelling for blank shape optimisation in sheet metal forming

Machine learning estimation of heat and mass transfer attributes of thermal radiative Williamson nanofluid flow via nonlinear stretchable surface

A data-driven approach for modeling large-amplitude flow-induced oscillations of elastically mounted pitching wings

Improving a Two-Equation Eddy-Viscosity Turbulence Model for High-Rayleigh-Number Natural-Convection Flows Using Machine Learning

Physics-informed neural networks for analyzing size effect and identifying parameters in piezoelectric semiconductor nanowires

Prediction of Airfoil Lift Coefficient Based on Multilayer Perceptron

Rapid CFD Prediction Based on Machine Learning Surrogate Model in Built Environment: A Review

An unsupervised machine-learning-based shock sensor: Application to high-order supersonic flow solvers

Multisource aerodynamic data reconstruction method using an enhanced multifidelity neural network

On the spatial prediction of the turbulent flow behind an array of cylinders via echo state networks

AI-Based Clustering of Numerical Flow Fields for Accelerating the Optimization of an Axial Turbine

Turbulent flow modeling in tur-bines via convolutional neural networks

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