# KRIGING IN TENSOR TRAIN DATA FORMAT

@article{Dolgov2019KRIGINGIT,
title={KRIGING IN TENSOR TRAIN DATA FORMAT},
author={Sergey V. Dolgov and Alexander Litvinenko and Dishi Liu},
journal={Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2019)},
year={2019}
}
• Published 21 April 2019
• Computer Science
• Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2019)
Combination of low-tensor rank techniques and the Fast Fourier transform (FFT) based methods had turned out to be prominent in accelerating various statistical operations such as Kriging, computing conditional covariance, geostatistical optimal design, and others. However, the approximation of a full tensor by its low-rank format can be computationally formidable. In this work, we incorporate the robust Tensor Train (TT) approximation of covariance matrices and the efficient TT-Cross algorithm…
3 Citations

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