Improving kriging surrogates of high-dimensional design models by Partial Least Squares dimension reduction

11/18/2020
by   jomorlier , et al.
0

Engineering computer codes are often computationally expensive. To lighten this load, we exploit new covariance kernels to replace computationally expensive codes with surrogate models. For input spaces with large dimensions, using the kriging model in the standard way is computationally expensive because a large covariance matrix must be inverted several times to estimate the parameters of the model. We address this issue herein by constructing a covariance kernel that depends on only a few parameters. The new kernel is constructed based on information obtained from the Partial Least Squares method. Promising results are obtained for numerical examples with up to 100 dimensions, and significant computational gain is obtained while maintaining sufficient accuracy.

READ FULL TEXT
research
06/08/2021

Sequential active learning of low-dimensional model representations for reliability analysis

To date, the analysis of high-dimensional, computationally expensive eng...
research
08/08/2017

Gradient-enhanced kriging for high-dimensional problems

Surrogate models provide a low computational cost alternative to evaluat...
research
04/19/2023

Constructing a simulation surrogate with partially observed output

Gaussian process surrogates are a popular alternative to directly using ...
research
08/22/2022

Sampling Gaussian Stationary Random Fields: A Stochastic Realization Approach

Generating large-scale samples of stationary random fields is of great i...
research
09/28/2019

A New Covariance Estimator for Sufficient Dimension Reduction in High-Dimensional and Undersized Sample Problems

The application of standard sufficient dimension reduction methods for r...
research
02/07/2018

Dimension Reduction Using Active Manifolds

Scientists and engineers rely on accurate mathematical models to quantif...
research
05/13/2019

Fast Parameter Inference in a Biomechanical Model of the Left Ventricle using Statistical Emulation

A central problem in biomechanical studies of personalised human left ve...

Please sign up or login with your details

Forgot password? Click here to reset