A hybrid model-data-driven framework for inverse load identification of interval structures based on physics-informed neural network and improved Kalman filter algorithm

Accurately capturing data on the external loads that large structural systems endure is crucial for improving the performance of energy equipment. This paper introduces a novel hybrid model-data-driven framework for the dynamic load identification of interval structures, which seamlessly combines fi...

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Bibliographic Details

Published in:	Applied energy Vol. 359; p. 122740
Main Authors:	Liu, Yaru, Wang, Lei, Ng, Bing Feng
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.04.2024
Subjects:	Dynamic load identification Interval structures Kalman filter algorithm Model-data-drive method Physics-informed neural network Dynamic load identification Model-data-drive method Physics-informed neural network Interval structures Kalman filter algorithm
ISSN:	0306-2619, 1872-9118
Online Access:	Get full text
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