Machine learning in aerodynamic shape optimization
Abstract Machine learning (ML) has been increasingly used to aid aerodynamic shape
optimization (ASO), thanks to the availability of aerodynamic data and continued …
optimization (ASO), thanks to the availability of aerodynamic data and continued …
Data-driven modeling for unsteady aerodynamics and aeroelasticity
Aerodynamic modeling plays an important role in multiphysics and design problems, in
addition to experiment and numerical simulation, due to its low-dimensional representation …
addition to experiment and numerical simulation, due to its low-dimensional representation …
Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders
K Lee, KT Carlberg - Journal of Computational Physics, 2020 - Elsevier
Nearly all model-reduction techniques project the governing equations onto a linear
subspace of the original state space. Such subspaces are typically computed using methods …
subspace of the original state space. Such subspaces are typically computed using methods …
Modal analysis of fluid flows: Applications and outlook
THE field of fluid mechanics involves a range of rich and vibrant problems with complex
dynamics stemming from instabilities, nonlinearities, and turbulence. The analysis of these …
dynamics stemming from instabilities, nonlinearities, and turbulence. The analysis of these …
Machine learning for fluid mechanics
SL Brunton, BR Noack… - Annual review of fluid …, 2020 - annualreviews.org
The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data
from experiments, field measurements, and large-scale simulations at multiple …
from experiments, field measurements, and large-scale simulations at multiple …
A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs
Conventional reduced order modeling techniques such as the reduced basis (RB) method
(relying, eg, on proper orthogonal decomposition (POD)) may incur in severe limitations …
(relying, eg, on proper orthogonal decomposition (POD)) may incur in severe limitations …
Reduced basis methods for time-dependent problems
Numerical simulation of parametrized differential equations is of crucial importance in the
study of real-world phenomena in applied science and engineering. Computational methods …
study of real-world phenomena in applied science and engineering. Computational methods …
Data-driven sparse sensor placement for reconstruction: Demonstrating the benefits of exploiting known patterns
Optimal sensor and actuator placement is an important unsolved problem in control theory.
Nearly every downstream control decision is affected by these sensor and actuator …
Nearly every downstream control decision is affected by these sensor and actuator …
Learning physics-based models from data: perspectives from inverse problems and model reduction
This article addresses the inference of physics models from data, from the perspectives of
inverse problems and model reduction. These fields develop formulations that integrate data …
inverse problems and model reduction. These fields develop formulations that integrate data …
Closed-loop turbulence control: Progress and challenges
SL Brunton, BR Noack - Applied Mechanics …, 2015 - asmedigitalcollection.asme.org
Closed-loop turbulence control is a critical enabler of aerodynamic drag reduction, lift
increase, mixing enhancement, and noise reduction. Current and future applications have …
increase, mixing enhancement, and noise reduction. Current and future applications have …