RT Journal Article
ID 136d80a0505dbb07
A1 Fuhrländer, Mona
A1 Georg, Niklas
A1 Römer, Ulrich
A1 Schöps, Sebastian
T1 YIELD OPTIMIZATION BASED ON ADAPTIVE NEWTON-MONTE CARLO AND POLYNOMIAL SURROGATES
JF International Journal for Uncertainty Quantification
JO IJUQ
YR 2020
FD 2020-07-30
VO 10
IS 4
SP 351
OP 373
K1 adaptivity
K1 failure probability
K1 Monte Carlo
K1 polynomial surrogates
K1 stochastic optimization
K1 stochastic sparse grid collocation
K1 uncertainty quantification
K1 yield analysis
AB In this paper we present an algorithm for yield estimation and optimization consisting of Hessian-based optimization methods, an adaptive Monte Carlo (MC) strategy, polynomial surrogates, and several error indicators. Yield estimation is used to quantify the impact of uncertainty in a manufacturing process. Since computational efficiency is one main issue in uncertainty quantification, we propose a hybrid method, where a large part of a MC sample is evaluated with a surrogate model, and only a small subset of the sample is reevaluated with a high-fidelity finite element model. In order to determine this critical fraction of the sample, an adjoint error indicator is used for both the surrogate error and the finite element error. For yield optimization we propose an adaptive Newton-MC method. We reduce computational effort and control the MC error by adaptively increasing the sample size. The proposed method minimizes the impact of uncertainty by optimizing the yield. It allows one to control the finite element error, surrogate error, and MC error. At the same time it is much more efficient than standard MC approaches combined with standard Newton algorithms.
PB Begell House
LK http://dl.begellhouse.com/journals/52034eb04b657aea,148a754a61aebe77,136d80a0505dbb07.html