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• W2782034465 abstract "Predictive analysis of complex computational models, such as uncertainty quantification (UQ), must often rely on using an existing database of simulation runs. In this paper we consider the task of performing low-multilinear-rank regression on such a database. Specifically we develop and analyze an efficient gradient computation that enables gradient-based optimization procedures, including stochastic gradient descent and quasi-Newton methods, for learning the parameters of a functional tensor-train (FT). We compare our algorithms with 22 other nonparametric and parametric regression methods on 10 real-world data sets and show that for many physical systems, exploiting low-rank structure facilitates efficient construction of surrogate models. We use a number of synthetic functions to build insight into behavior of our algorithms, including the rank adaptation and group-sparsity regularization procedures that we developed to reduce overfitting. Finally we conclude the paper by building a surrogate of a physical model of a propulsion plant on a naval vessel." @default.
• W2782034465 created "2018-01-12" @default.
• W2782034465 creator A5021570608 @default.
• W2782034465 creator A5084145417 @default.
• W2782034465 date "2018-12-01" @default.
• W2782034465 modified "2023-10-15" @default.
• W2782034465 title "Gradient-based optimization for regression in the functional tensor-train format" @default.
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• W2782034465 doi "https://doi.org/10.1016/j.jcp.2018.08.010" @default.
• W2782034465 hasPublicationYear "2018" @default.
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