Generalized polynomial chaos-informed efficient stochastic Kriging

Stochastic kriging (SK) offers an explicit way to characterize heterogeneous noise variance in stochastic computer simulations and has gained considerable traction recently as a surrogate model. Nevertheless, SK relies on tedious Monte Carlo (MC) method to estimate the intrinsic variance at each design input. For computationally expensive simulations, the substantial replication effort has essentially rendered SK intractable. To this end, we develop generalized polynomial chaos (gPC)-informed efficient stochastic kriging (gPC-SK) to ameliorate the computational cost. At its core, gPC supplants the tedious repetitive MC simulations, instead resting on a much smaller set of sampling points to estimate the intrinsic uncertainty, thus applicable to those prohibitively expensive simulations. We present the gPC-SK in sequential optimal design on the borehole function and stability of time-delay dynamic systems. (C) 2021 Elsevier Inc. All rights reserved.

Automated summary

Stochastic kriging (SK) provides an explicit way to characterize heterogeneous noise variance in stochastic computer simulations, but relies on tedious Monte Carlo (MC) method. Therefore, researchers have developed efficient stochastic kriging informed by generalized polynomial chaos (gPC-SK) to reduce computational costs.