Non-stationary kriging for design optimization

Wave data assimilation using non-stationary kriging

We present a fast sequential assimilation technique for conditioning wave simulations using buoy data. It is based on non-stationary kriging that uses a separable space-time variogram. This variogram is estimated from the history of simulations, as a collection of maps of variances of increments between the logarithms of simulated and observed wave heights. The method finally provides a set of ...

Sampling Design Optimization for Space-Time Kriging

A non-stationary covariance-based Kriging method for metamodelling in engineering design

Metamodels are widely used to facilitate the analysis and optimization of engineering systems that involve computationally expensive simulations. Kriging is a metamodelling technique that is well known for its ability to build surrogate models of responses with non-linear behaviour. However, the assumption of a stationary covariance structure underlying Kriging does not hold in situations where...

Non-Stationary Stochastic Optimization

We consider a non-stationary variant of a sequential stochastic optimization problem, in which the underlying cost functions may change along the horizon. We propose a measure, termed variation budget, that controls the extent of said change, and study how restrictions on this budget impact achievable performance. We identify sharp conditions under which it is possible to achieve long-run avera...

Metamodeling Method Using Dynamic Kriging for Design Optimization

Metamodeling has been widely used for design optimization by building surrogate models for computationally intensive engineering application problems. Among all the metamodeling methods, the kriging method has gained significant interest for its accuracy.However, in traditional krigingmethods, themean structure is constructed using a fixed set of polynomial basis functions, and the optimization...