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Optimization of computationally expensive simulations with Gaussian processes and parameter uncertainty: Application to cardiovascular surgery
2012
2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton)
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In many applications of simulation 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 optimization difficult. Motivated by an application in the design of grafts for heart surgery with uncertainty about input parameters, we use Bayesian methods to design an algorithm that exploits this random vector's lowdimensionality to improve
doi:10.1109/allerton.2012.6483247
dblp:conf/allerton/XieFSME12
fatcat:aq4y4fdf3fevjfwzllg3mvj7n4