[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Clark Jr et al., 2016 - Google Patents

Engineering design exploration using locally optimized covariance kriging

Clark Jr et al., 2016

Document ID
4120338472494936933
Author
Clark Jr D
Bae H
Gobal K
Penmetsa R
Publication year
Publication venue
AIAA Journal

External Links

Snippet

Surrogate models are used in many engineering applications where actual function evaluations are computationally expensive. Kriging is a flexible surrogate model best suited for interpolating nonlinear system responses with a limited number of training points. It is …
Continue reading at arc.aiaa.org (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50—Computer-aided design
    • G06F17/5009—Computer-aided design using simulation
    • G06F17/5018—Computer-aided design using simulation using finite difference methods or finite element methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50—Computer-aided design
    • G06F17/5086—Mechanical design, e.g. parametric or variational design
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F2217/00—Indexing scheme relating to computer aided design [CAD]
    • G06F2217/16—Numerical modeling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F2217/00—Indexing scheme relating to computer aided design [CAD]
    • G06F2217/46—Fuselage
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computer systems based on specific mathematical models
    • G06N7/005—Probabilistic networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F15/00—Digital computers in general; Data processing equipment in general
    • G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F1/00—Details of data-processing equipment not covered by groups G06F3/00 - G06F13/00, e.g. cooling, packaging or power supply specially adapted for computer application
    • G06F1/16—Constructional details or arrangements

Similar Documents

Publication Publication Date Title
Clark Jr et al. Engineering design exploration using locally optimized covariance kriging
Li et al. Data-based approach for fast airfoil analysis and optimization
Yu et al. Flowfield reconstruction method using artificial neural network
Giselle Fernández-Godino et al. Issues in deciding whether to use multifidelity surrogates
Tran et al. sMF-BO-2CoGP: A sequential multi-fidelity constrained Bayesian optimization framework for design applications
Sabater et al. Fast predictions of aircraft aerodynamics using deep-learning techniques
Dupuis et al. Surrogate modeling of aerodynamic simulations for multiple operating conditions using machine learning
Zhao et al. Metamodeling method using dynamic kriging for design optimization
Iuliano et al. Proper orthogonal decomposition, surrogate modelling and evolutionary optimization in aerodynamic design
Bailly et al. Multifidelity aerodynamic optimization of a helicopter rotor blade
Iuliano Global optimization of benchmark aerodynamic cases using physics-based surrogate models
Liu et al. Modeling multiresponse surfaces for airfoil design with multiple-output-Gaussian-process regression
Chen et al. Multimodel fusion based sequential optimization
Cai et al. An enhanced RBF-HDMR integrated with an adaptive sampling method for approximating high dimensional problems in engineering design
Sen et al. Evaluation of multifidelity surrogate modeling techniques to construct closure laws for drag in shock–particle interactions
Jiang et al. Variable-fidelity lower confidence bounding approach for engineering optimization problems with expensive simulations
Ghoreishi et al. Adaptive dimensionality reduction for fast sequential optimization with gaussian processes
Duvigneau et al. Kriging‐based optimization applied to flow control
Han et al. Surrogate-based aerodynamic shape optimization with application to wind turbine airfoils
Satria Palar et al. Gaussian process surrogate model with composite kernel learning for engineering design
Bae et al. Nondeterministic kriging for engineering design exploration
Renganathan Koopman-based approach to nonintrusive reduced order modeling: Application to aerodynamic shape optimization and uncertainty propagation
Kelly et al. Data-driven modeling approach for mistuned cyclic structures
Bevan et al. Adaptive surrogate-based optimization of vortex generators for tiltrotor geometry
Lin et al. Gradient-enhanced multi-output gaussian process model for simulation-based engineering design