Subber, W.; Ghosh, S.; Pandita, P.; Zhang, Y.; Wang, L. Data-Informed Decomposition for Localized Uncertainty Quantification of Dynamical Systems. Preprints2020, 2020080358. https://doi.org/10.20944/preprints202008.0358.v1
APA Style
Subber, W., Ghosh, S., Pandita, P., Zhang, Y., & Wang, L. (2020). Data-Informed Decomposition for Localized Uncertainty Quantification of Dynamical Systems. Preprints. https://doi.org/10.20944/preprints202008.0358.v1
Chicago/Turabian Style
Subber, W., Yiming Zhang and Liping Wang. 2020 "Data-Informed Decomposition for Localized Uncertainty Quantification of Dynamical Systems" Preprints. https://doi.org/10.20944/preprints202008.0358.v1
Abstract
Industrial dynamical systems often exhibit multi-scale response due to material heterogeneities, operation conditions and complex environmental loadings. In such problems, it is the case that the smallest length-scale of the systems dynamics controls the numerical resolution required to effectively resolve the embedded physics. In practice however, high numerical resolutions is only required in a confined region of the system where fast dynamics or localized material variability are exhibited, whereas a coarser discretization can be sufficient in the rest majority of the system. To this end, a unified computational scheme with uniform spatio-temporal resolutions for uncertainty quantification can be very computationally demanding. Partitioning the complex dynamical system into smaller easier-to-solve problems based of the localized dynamics and material variability can reduce the overall computational cost. However, identifying the region of interest for high-resolution and intensive uncertainty quantification can be a problem dependent. The region of interest can be specified based on the localization features of the solution, user interest, and correlation length of the random material properties. For problems where a region of interest is not evident, Bayesian inference can provide a feasible solution. In this work, we employ a Bayesian framework to update our prior knowledge on the localized region of interest using measurements and system response. To address the computational cost of the Bayesian inference, we construct a Gaussian process surrogate for the forward model. Once, the localized region of interest is identified, we use polynomial chaos expansion to propagate the localization uncertainty. We demonstrate our framework through numerical experiments on a three-dimensional elastodynamic problem
Keywords
Bayesian inference; uncertainty quantification; dynamical systems; inverse problem; system identification; Gaussian process regression; polynomial chaos
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
