Turbulence Regression

• URL: http://arxiv.org/abs/2512.05483v1
• Date: Fri, 05 Dec 2025 07:20:44 GMT
• Title: Turbulence Regression
• Authors: Yingang Fan, Binjie Ding, Baiyi Chen,
• Abstract summary: This paper introduces a NeuTucker decomposition model utilizing discretized data.<n>It constructs a low-rank Tucker decomposition model based on a Tucker neural network to capture latent interactions within the three-dimensional wind field data.<n>In estimating missing observations in real datasets, this discretized NeuTucF model demonstrates superior performance compared to various common regression models.
• Score: 0.4779196219827507
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Air turbulence refers to the disordered and irregular motion state generated by drastic changes in velocity, pressure, or direction during airflow. Various complex factors lead to intricate low-altitude turbulence outcomes. Under current observational conditions, especially when using only wind profile radar data, traditional methods struggle to accurately predict turbulence states. Therefore, this paper introduces a NeuTucker decomposition model utilizing discretized data. Designed for continuous yet sparse three-dimensional wind field data, it constructs a low-rank Tucker decomposition model based on a Tucker neural network to capture the latent interactions within the three-dimensional wind field data. Therefore, two core ideas are proposed here: 1) Discretizing continuous input data to adapt to models like NeuTucF that require discrete data inputs. 2) Constructing a four-dimensional Tucker interaction tensor to represent all possible spatio-temporal interactions among different elevations and three-dimensional wind speeds. In estimating missing observations in real datasets, this discretized NeuTucF model demonstrates superior performance compared to various common regression models.

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