State-of-the-art and comparative review of adaptive sampling methods for kriging
Metamodels aim to approximate characteristics of functions or systems from the knowledge
extracted on only a finite number of samples. In recent years kriging has emerged as a …
extracted on only a finite number of samples. In recent years kriging has emerged as a …
Advances in surrogate based modeling, feasibility analysis, and optimization: A review
A Bhosekar, M Ierapetritou - Computers & Chemical Engineering, 2018 - Elsevier
The idea of using a simpler surrogate to represent a complex phenomenon has gained
increasing popularity over past three decades. Due to their ability to exploit the black-box …
increasing popularity over past three decades. Due to their ability to exploit the black-box …
Hyperparameter search in machine learning
We introduce the hyperparameter search problem in the field of machine learning and
discuss its main challenges from an optimization perspective. Machine learning methods …
discuss its main challenges from an optimization perspective. Machine learning methods …
Polynomial-chaos-based Kriging
Computer simulation has become the standard tool in many engineering fields for designing
and optimizing systems, as well as for assessing their reliability. Optimization and …
and optimizing systems, as well as for assessing their reliability. Optimization and …
Surrogate-assisted reliability-based design optimization: a survey and a unified modular framework
M Moustapha, B Sudret - Structural and Multidisciplinary Optimization, 2019 - Springer
Reliability-based design optimization (RBDO) is an active field of research with an ever
increasing number of contributions. Numerous methods have been proposed for the solution …
increasing number of contributions. Numerous methods have been proposed for the solution …
Improving kriging surrogates of high-dimensional design models by Partial Least Squares dimension reduction
Engineering computer codes are often computationally expensive. To lighten this load, we
exploit new covariance kernels to replace computationally expensive codes with surrogate …
exploit new covariance kernels to replace computationally expensive codes with surrogate …
A new surrogate modeling technique combining Kriging and polynomial chaos expansions–Application to uncertainty analysis in computational dosimetry
In numerical dosimetry, the recent advances in high performance computing led to a strong
reduction of the required computational time to assess the specific absorption rate (SAR) …
reduction of the required computational time to assess the specific absorption rate (SAR) …
Metamodel-based sensitivity analysis: polynomial chaos expansions and Gaussian processes
Global sensitivity analysis is now established as a powerful approach for determining the
key random input parameters that drive the uncertainty of model output predictions. Yet the …
key random input parameters that drive the uncertainty of model output predictions. Yet the …
Replication or exploration? Sequential design for stochastic simulation experiments
We investigate the merits of replication, and provide methods for optimal design (including
replicates), with the goal of obtaining globally accurate emulation of noisy computer …
replicates), with the goal of obtaining globally accurate emulation of noisy computer …
Structural reliability analysis for p-boxes using multi-level meta-models
In modern engineering, computer simulations are a popular tool to analyse, design, and
optimize systems. Furthermore, concepts of uncertainty and the related reliability analysis …
optimize systems. Furthermore, concepts of uncertainty and the related reliability analysis …