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 …
[HTML][HTML] Adaptive approaches in metamodel-based reliability analysis: A review
The present work reviews the implementation of adaptive metamodeling for reliability
analysis with emphasis in four main types of metamodels: response surfaces, polynomial …
analysis with emphasis in four main types of metamodels: response surfaces, polynomial …
Bio-inspired computation: Where we stand and what's next
In recent years, the research community has witnessed an explosion of literature dealing
with the mimicking of behavioral patterns and social phenomena observed in nature towards …
with the mimicking of behavioral patterns and social phenomena observed in nature towards …
State-of-the-art and comparative review of adaptive sampling methods for kriging
Metamodels aim to approximate characteristics of functions or systems from the knowledge
extracted on only a finite number of samples. In recent years kriging has emerged as a …
extracted on only a finite number of samples. In recent years kriging has emerged as a …
Advances in surrogate based modeling, feasibility analysis, and optimization: A review
A Bhosekar, M Ierapetritou - Computers & Chemical Engineering, 2018 - Elsevier
The idea of using a simpler surrogate to represent a complex phenomenon has gained
increasing popularity over past three decades. Due to their ability to exploit the black-box …
increasing popularity over past three decades. Due to their ability to exploit the black-box …
A Python surrogate modeling framework with derivatives
The surrogate modeling toolbox (SMT) is an open-source Python package that contains a
collection of surrogate modeling methods, sampling techniques, and benchmarking …
collection of surrogate modeling methods, sampling techniques, and benchmarking …
Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial
On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …
essential layer of safety assurance that could lead to more principled decision making by …
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 …
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 …
Efficient aerodynamic shape optimization with deep-learning-based geometric filtering
Surrogate-based optimization has been used in aerodynamic shape optimization, but it has
been limited due to the curse of dimensionality. Although a large number of variables are …
been limited due to the curse of dimensionality. Although a large number of variables are …