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 …
A review on design of experiments and surrogate models in aircraft real-time and many-query aerodynamic analyses
Full scale aerodynamic wind tunnel testing, numerical simulation of high dimensional (full-
order) aerodynamic models or flight testing are some of the fundamental but complex steps …
order) aerodynamic models or flight testing are some of the fundamental but complex steps …
Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification
RK Tripathy, I Bilionis - Journal of computational physics, 2018 - Elsevier
State-of-the-art computer codes for simulating real physical systems are often characterized
by vast number of input parameters. Performing uncertainty quantification (UQ) tasks with …
by vast number of input parameters. Performing uncertainty quantification (UQ) tasks with …
A survey on high-dimensional Gaussian process modeling with application to Bayesian optimization
Bayesian Optimization (BO), the application of Bayesian function approximation to finding
optima of expensive functions, has exploded in popularity in recent years. In particular, much …
optima of expensive functions, has exploded in popularity in recent years. In particular, much …
[图书][B] Active subspaces: Emerging ideas for dimension reduction in parameter studies
PG Constantine - 2015 - SIAM
Parameter studies are everywhere in computational science. Complex engineering
simulations must run several times with different inputs to effectively study the relationships …
simulations must run several times with different inputs to effectively study the relationships …
Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks
Stochastic partial differential equations (SPDEs) are ubiquitous in engineering and
computational sciences. The stochasticity arises as a consequence of uncertainty in input …
computational sciences. The stochasticity arises as a consequence of uncertainty in input …
Airfoil design parameterization and optimization using bézier generative adversarial networks
Global optimization of aerodynamic shapes usually requires a large number of expensive
computational fluid dynamics simulations because of the high dimensionality of the design …
computational fluid dynamics simulations because of the high dimensionality of the design …
Increasing the scope as you learn: Adaptive Bayesian optimization in nested subspaces
L Papenmeier, L Nardi… - Advances in Neural …, 2022 - proceedings.neurips.cc
Recent advances have extended the scope of Bayesian optimization (BO) to expensive-to-
evaluate black-box functions with dozens of dimensions, aspiring to unlock impactful …
evaluate black-box functions with dozens of dimensions, aspiring to unlock impactful …
Enhanced data efficiency using deep neural networks and Gaussian processes for aerodynamic design optimization
Adjoint-based optimization methods are attractive for aerodynamic shape design primarily
due to their computational costs being independent of the dimensionality of the input space …
due to their computational costs being independent of the dimensionality of the input space …
Aerodynamic design optimization and shape exploration using generative adversarial networks
Global optimization of aerodynamic shapes requires a large number of expensive CFD
simulations because of the high dimensionality of the design space. One means to combat …
simulations because of the high dimensionality of the design space. One means to combat …