Anomaly detection in scientific data using joint statistical moments

DOI10.1016/j.jcp.2019.03.003zbMath1453.62499arXiv1808.04498OpenAlexW2885414839WikidataQ128232592 ScholiaQ128232592MaRDI QIDQ2220587

Julia Ling, Warren L. Davis, Hemanth Kolla, W. Philip Kegelmeyer, Konduri Aditya, Timothy M. Shead

Publication date: 25 January 2021

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1808.04498

zbMATH Keywords

Hellinger distancescientific computingtensor decompositionanomaly detectionautoignitioncokurtosis

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Statistical turbulence modeling (76F55) Turbulent combustion; reactive turbulence (76F80)

Related Items (1)

Estimating Higher-Order Moments Using Symmetric Tensor Decomposition

Uses Software

• TensorToolbox
• Scikit
• LOF

Cites Work

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• Tensor Decompositions and Applications
• Numerically stable, scalable formulas for parallel and online computation of higher-order multivariate central moments with arbitrary weights
• Trigger Detection for Adaptive Scientific Workflows Using Percentile Sampling
• Tensor decompositions for learning latent variable models
• Three-dimensional direct numerical simulation of a turbulent lifted hydrogen jet flame in heated coflow: a chemical explosive mode analysis
• A Multilinear Singular Value Decomposition
• Symmetric Tensors and Symmetric Tensor Rank
• Kurtosis as Peakedness, 1905–2014.R.I.P.

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