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2023 | OriginalPaper | Chapter

7. Variational Filter for Predictive Modeling of Structural Systems

Authors : Alana Lund, Ilias Bilionis, Shirley J. Dyke

Published in: Model Validation and Uncertainty Quantification, Volume 3

Publisher: Springer International Publishing

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Abstract

Bayesian inference offers a distinct advantage in predictive structural modeling as it quantifies the inherent epistemic uncertainties that arise due to observations of the system which are both finite in length and limited in representative behavioral information. Current research interest in the field of predictive structural modeling has emphasized analytical and sampling approaches to Bayesian inference, which have the respective advantages of either computational speed or inference accuracy. Recent work in optimization-based inference approaches have created new opportunities to balance these advantages and generate flexible, efficient, and scalable filters for joint parameter-state identification of complex nonlinear structural systems. These techniques, commonly referred to as variational inference, infer the hidden states of a system by attempting to match the true posterior and to a parameterized distribution. In this study we build on the theory of automatic differentiation variational inference to introduce a novel approach to variational filtering for the identification of complex structural systems. We evaluate our method using experimental observations from a nonlinear energy sink device subject to base excitation. Comparison between identification performed using our approach and the unscented Kalman filter reveals the utility of the variational filtering technique in terms of both flexibility in the stochastic model and robustness of the method to poor specification of prior uncertainty.

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Literature
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Metadata
Title
Variational Filter for Predictive Modeling of Structural Systems
Authors
Alana Lund
Ilias Bilionis
Shirley J. Dyke
Copyright Year
2023
DOI
https://doi.org/10.1007/978-3-031-04090-0_7