Data-Informed Decomposition for Localized Uncertainty Quantification of Dynamical Systems

TL;DR

This paper introduces a Bayesian-based, data-informed approach for localized uncertainty quantification in complex dynamical systems, reducing computational costs by focusing on regions of interest identified through surrogate modeling.

Contribution

It presents a novel framework combining Bayesian inference, Gaussian process surrogates, and polynomial chaos to efficiently identify and analyze localized regions of interest in dynamical systems.

Findings

Effective localization of regions of interest using Bayesian inference.

Significant reduction in computational cost for uncertainty quantification.

Successful demonstration on a 3D elastodynamic problem.

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.