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Covariance matrix testing in high dimension using random projections
Estimation and hypothesis tests for the covariance matrix in high dimens...
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Gradient-enhanced kriging for high-dimensional problems
Surrogate models provide a low computational cost alternative to evaluat...
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Sensitivity Analysis for Computationally Expensive Models using Optimization and Objective-oriented Surrogate Approximations
In this paper, we focus on developing efficient sensitivity analysis met...
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A machine learning framework for computationally expensive transient models
The promise of machine learning has been explored in a variety of scient...
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Dimension Reduction Using Active Manifolds
Scientists and engineers rely on accurate mathematical models to quantif...
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Data-efficient Neuroevolution with Kernel-Based Surrogate Models
Surrogate-assistance approaches have long been used in computationally e...
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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...
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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 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.
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