A multilevel simulation optimization approach for quantile functions
A quantile is a popular performance measure for a stochastic system to evaluate its
variability and risk. To reduce the risk, selecting the actions that minimize the tail quantiles of …
variability and risk. To reduce the risk, selecting the actions that minimize the tail quantiles of …
Efficient global estimation of conditional-value-at-risk through stochastic kriging and extreme value theory
We consider the problem of evaluating risk for a system that is modeled by a complex
stochastic simulation with many possible input parameter values. Two sources of …
stochastic simulation with many possible input parameter values. Two sources of …
Metamodel-assisted risk analysis for stochastic simulation with input uncertainty
For complex stochastic systems, simulation can be used to study the system inherent risk
behaviors characterized by a sequence of percentiles. In this paper, we develop a Bayesian …
behaviors characterized by a sequence of percentiles. In this paper, we develop a Bayesian …
Multi-response gaussian process for multidisciplinary design optimization
Cov= covariance B= the matrix of covariance between responses G (a, b)= Gaussian
process with follows mean a and variance bn= the number of observed samples p= the …
process with follows mean a and variance bn= the number of observed samples p= the …
Quantile simulation optimization with stochastic co-kriging model
Although the mean is a widely-used performance measure for stochastic simulations, the
quantiles have become very attractive to measure the variability and the risk of the simulated …
quantiles have become very attractive to measure the variability and the risk of the simulated …
Efficient Global Optimization of Multidisciplinary System using Variable Fidelity Analysis and Dynamic Sampling Method
J Park - 2019 - vtechworks.lib.vt.edu
Work in this dissertation is motivated by reducing the design cost at the early design stage
while maintaining high design accuracy throughout all design stages. It presents four key …
while maintaining high design accuracy throughout all design stages. It presents four key …
Metamodeling and Optimization with Gaussian Process Models for Stochastic Simulations
W Songhao - 2019 - search.proquest.com
This thesis proposes three pieces of work on the Gaussian process (GP) model and GP-
based Bayesian optimization (BO) for stochastic simulations. Firstly, we propose a multi …
based Bayesian optimization (BO) for stochastic simulations. Firstly, we propose a multi …