相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。Non-intrusive reduced order modeling for flowfield reconstruction based on residual neural network
Wenjun Ma et al.
ACTA ASTRONAUTICA (2021)
Turbulence closure for high Reynolds number airfoil flows by deep neural networks
Linyang Zhu et al.
AEROSPACE SCIENCE AND TECHNOLOGY (2021)
Supervised learning method for the physical field reconstruction in a nanofluid heat transfer problem
Tianyuan Liu et al.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER (2021)
A point-cloud deep learning framework for prediction of fluid flow fields on irregular geometries
Ali Kashefi et al.
PHYSICS OF FLUIDS (2021)
Enhanced data efficiency using deep neural networks and Gaussian processes for aerodynamic design optimization
S. Ashwin Renganathan et al.
AEROSPACE SCIENCE AND TECHNOLOGY (2021)
Surrogate-based aerodynamic shape optimization for delaying airfoil dynamic stall using Kriging regression and infill criteria
Vishal Raul et al.
AEROSPACE SCIENCE AND TECHNOLOGY (2021)
Dual-convolutional neural network based aerodynamic prediction and multi-objective optimization of a compact turbine rotor
Yuqi Wang et al.
AEROSPACE SCIENCE AND TECHNOLOGY (2021)
Rapid airfoil design optimization via neural networks-based parameterization and surrogate modeling
Xiaosong Du et al.
AEROSPACE SCIENCE AND TECHNOLOGY (2021)
A deep learning based prediction approach for the supercritical airfoil at transonic speeds
Di Sun et al.
PHYSICS OF FLUIDS (2021)
Flow field prediction of supercritical airfoils via variational autoencoder based deep learning framework
Jing Wang et al.
PHYSICS OF FLUIDS (2021)
CNNFOIL: convolutional encoder decoder modeling for pressure fields around airfoils
Cihat Duru et al.
NEURAL COMPUTING & APPLICATIONS (2021)
Machine Learning for Fluid Mechanics
Steven L. Brunton et al.
ANNUAL REVIEW OF FLUID MECHANICS, VOL 52 (2020)
A deep learning approach for efficiently and accurately evaluating the flow field of supercritical airfoils
Haizhou Wu et al.
COMPUTERS & FLUIDS (2020)
Machine learning for nonintrusive model order reduction of the parametric inviscid transonic flow past an airfoil
S. Ashwin Renganathan et al.
PHYSICS OF FLUIDS (2020)
Fast pressure distribution prediction of airfoils using deep learning
Xinyu Hui et al.
AEROSPACE SCIENCE AND TECHNOLOGY (2020)
Airfoil Design Parameterization and Optimization Using Bezier Generative Adversarial Networks
Wei Chen et al.
AIAA JOURNAL (2020)
CNN-LSTM based reduced order modeling of two-dimensional unsteady flows around a circular cylinder at different Reynolds numbers
Kazuto Hasegawa et al.
FLUID DYNAMICS RESEARCH (2020)
Deep neural network approach for fault detection and diagnosis during startup transient of liquid-propellant rocket engine
Soon-Young Park et al.
ACTA ASTRONAUTICA (2020)
Transition effects on flow characteristics around a static two-dimensional airfoil
Rui Wang et al.
PHYSICS OF FLUIDS (2020)
Benchmark aerodynamic shape optimization with the POD-based CST airfoil parametric method
Xiaojing Wu et al.
AEROSPACE SCIENCE AND TECHNOLOGY (2019)
Fast flow field prediction over airfoils using deep learning approach
Vinothkumar Sekar et al.
PHYSICS OF FLUIDS (2019)
Framework for design optimization using deep reinforcement learning
Kazuo Yonekura et al.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION (2019)
Convolutional neural network based combustion mode classification for condition monitoring in the supersonic combustor
Xiaobin Zhu et al.
ACTA ASTRONAUTICA (2019)
Prediction of aerodynamic flow fields using convolutional neural networks
Saakaar Bhatnagar et al.
COMPUTATIONAL MECHANICS (2019)
Application of deep learning based multi-fidelity surrogate model to robust aerodynamic design optimization
Jun Tao et al.
AEROSPACE SCIENCE AND TECHNOLOGY (2019)
Data-driven prediction of unsteady flow over a circular cylinder using deep learning
Sangseung Lee et al.
JOURNAL OF FLUID MECHANICS (2019)
A hybrid reduced-order framework for complex aeroelastic simulations
Jiaqing Kou et al.
AEROSPACE SCIENCE AND TECHNOLOGY (2019)
Flowfield Reconstruction Method Using Artificial Neural Network
Jian Yu et al.
AIAA JOURNAL (2019)
Machine learning methods for turbulence modeling in subsonic flows around airfoils
Linyang Zhu et al.
PHYSICS OF FLUIDS (2019)
A novel spatial-temporal prediction method for unsteady wake flows based on hybrid deep neural network
Renkun Han et al.
PHYSICS OF FLUIDS (2019)
A review of parametric approaches specific to aerodynamic design process
Tian-tian Zhang et al.
ACTA ASTRONAUTICA (2018)
State-of-the-art in aerodynamic shape optimisation methods
S. N. Skinner et al.
APPLIED SOFT COMPUTING (2018)
Topology optimization with closed B-splines and Boolean operations
Weihong Zhang et al.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2017)
Support Vector Machines for classification and regression
Richard G. Brereton et al.
ANALYST (2010)
Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions
Songqing Shan et al.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION (2010)
An adaptive radial basis algorithm (ARBF) for expensive black-box global optimization
Kenneth Holmstrom
JOURNAL OF GLOBAL OPTIMIZATION (2008)
Efficient optimization design method using kriging model
S Jeong et al.
JOURNAL OF AIRCRAFT (2005)