Name
Title | ||
|---|---|---|
Optimization of computationally expensive simulations with Gaussian processes and parameter uncertainty: Application to cardiovascular surgery |
Abstract | ||
|---|---|---|
In many applications of simulation-based optimization, the random output variable whose expectation is being optimized is a deterministic function of a low-dimensional random vector. This deterministic function is often expensive to compute, making simulation-based optimization difficult. Motivated by an application in the design of bypass grafts for cardiovascular surgery with uncertainty about input parameters, we use Bayesian methods to design an algorithm that exploits this random vector's low-dimensionality to improve performance. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/Allerton.2012.6483247 | Allerton Conference |
Keywords | DocType | ISSN |
optimisation,random output variable,random processes,bayesian method,bayes methods,cardiovascular system,gaussian process,deterministic function,gaussian processes,parameter uncertainty,low-dimensional random vector,bypass graft,simulation-based optimization,cardiovascular surgery,surgery | Conference | 2474-0195 |
ISBN | Citations | PageRank |
978-1-4673-4537-8 | 4 | 0.51 |
References | Authors | |
6 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing Xie | 1 | 25 | 1.69 |
| Peter I. Frazier | 2 | 606 | 46.34 |
| Sethuraman Sankaran | 3 | 16 | 2.46 |
| Alison Marsden | 4 | 52 | 8.83 |
| Saleh Elmohamed | 5 | 4 | 0.51 |