Quantile surrogates and sensitivity by adaptive Gaussian process for efficient reliability-based design optimization
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Quantile surrogates and sensitivity by adaptive Gaussian process for efficient reliability-based design optimization
Authors
Keywords
Active learning, Design of experiments, Gaussian process, Quantile surrogates, Reliability-based design optimization, Surrogate sensitivity
Journal
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Volume 161, Issue -, Pages 107962
Publisher
Elsevier BV
Online
2021-05-01
DOI
10.1016/j.ymssp.2021.107962
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Reliability-Based Design Optimization Using Quantile Surrogates by Adaptive Gaussian Process
- (2021) Jungho Kim et al. JOURNAL OF ENGINEERING MECHANICS
- First-passage probability estimation by Poisson branching process model
- (2021) Sang-ri Yi et al. STRUCTURAL SAFETY
- Probabilistic evaluation of seismic responses using deep learning method
- (2020) Taeyong Kim et al. STRUCTURAL SAFETY
- A novel active learning-based Gaussian process metamodelling strategy for estimating the full probability distribution in forward UQ analysis
- (2020) Ziqi Wang et al. STRUCTURAL SAFETY
- Structural optimization considering dynamic reliability constraints via probability density evolution method and change of probability measure
- (2020) Jianbing Chen et al. STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
- Probability-Adaptive Kriging in n-Ball (PAK-Bn) for reliability analysis
- (2020) Jungho Kim et al. STRUCTURAL SAFETY
- An adaptive scheme for reliability-based global design optimization: A Markov chain Monte Carlo approach
- (2020) H.A. Jensen et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Surrogate Model Uncertainty Quantification for Reliability-based Design Optimization
- (2019) Mingyang Li et al. RELIABILITY ENGINEERING & SYSTEM SAFETY
- Surrogate-assisted reliability-based design optimization: a survey and a unified modular framework
- (2019) Maliki Moustapha et al. STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
- Gaussian mixture–based equivalent linearization method (GM‐ELM) for fragility analysis of structures under nonstationary excitations
- (2019) Sang‐ri Yi et al. EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS
- Design of structural monitoring sensor network using surrogate modeling of stochastic sensor signal
- (2019) Amin Toghi Eshghi et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Deep learning for high-dimensional reliability analysis
- (2019) Mingyang Li et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- An active-learning algorithm that combines sparse polynomial chaos expansions and bootstrap for structural reliability analysis
- (2018) Stefano Marelli et al. STRUCTURAL SAFETY
- High-Dimensional Reliability-Based Design Optimization Involving Highly Nonlinear Constraints and Computationally Expensive Simulations
- (2018) Meng Li et al. JOURNAL OF MECHANICAL DESIGN
- System-reliability-based design and topology optimization of structures under constraints on first-passage probability
- (2018) Junho Chun et al. STRUCTURAL SAFETY
- Reliability-based design optimization using the directional bat algorithm
- (2017) Asma Chakri et al. NEURAL COMPUTING & APPLICATIONS
- A local Kriging approximation method using MPP for reliability-based design optimization
- (2016) Xiaoke Li et al. COMPUTERS & STRUCTURES
- An efficient framework for the reliability-based design optimization of large-scale uncertain and stochastic linear systems
- (2016) Seymour M.J. Spence et al. PROBABILISTIC ENGINEERING MECHANICS
- Quantile-based optimization under uncertainties using adaptive Kriging surrogate models
- (2016) Maliki Moustapha et al. STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
- Structural topology optimization under constraints on instantaneous failure probability
- (2015) Junho Chun et al. STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
- Kriging metamodeling for approximation of high-dimensional wave and surge responses in real-time storm/hurricane risk assessment
- (2013) Gaofeng Jia et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- An adaptive decoupling approach for reliability-based design optimization
- (2013) Zhenzhong Chen et al. COMPUTERS & STRUCTURES
- AK-SYS: An adaptation of the AK-MCS method for system reliability
- (2013) W. Fauriat et al. RELIABILITY ENGINEERING & SYSTEM SAFETY
- Sampling-Based Stochastic Sensitivity Analysis Using Score Functions for RBDO Problems With Correlated Random Variables
- (2011) Ikjin Lee et al. JOURNAL OF MECHANICAL DESIGN
- Large scale reliability-based design optimization of wind excited tall buildings
- (2011) Seymour M.J. Spence et al. PROBABILISTIC ENGINEERING MECHANICS
- Reliability-based design optimization using kriging surrogates and subset simulation
- (2011) Vincent Dubourg et al. STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
- Single-Loop System Reliability-Based Design Optimization Using Matrix-Based System Reliability Method: Theory and Applications
- (2010) Tam H. Nguyen et al. JOURNAL OF MECHANICAL DESIGN
- Computational methods in optimization considering uncertainties – An overview
- (2008) G.I. Schuëller et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Aleatory or epistemic? Does it matter?
- (2008) Armen Der Kiureghian et al. STRUCTURAL SAFETY
- A sampling technique enhancing accuracy and efficiency of metamodel-based RBDO: Constraint boundary sampling
- (2007) T.H. Lee et al. COMPUTERS & STRUCTURES
- Designing robust structures – A nonlinear simulation based approach
- (2007) Michael Beer et al. COMPUTERS & STRUCTURES
- Reliable design space and complete single-loop reliability-based design optimization
- (2007) Songqing Shan et al. RELIABILITY ENGINEERING & SYSTEM SAFETY
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started