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Applied Mechanics, Volume 1, Issue 3

2020 September - 1 articles

Cover Story: Uncertainty quantification (UQ) to quantify the effect of uncertainty on model predictions is essential to improve the accuracy of computational models. The polynomial chaos expansion (PCE), one of the most popular UQ techniques, has gained increasing attention lately. The key to the successful application of PCE is stochastic Galerkin projection, which yields coupled deterministic models of PCE coefficients to describe a stochastic system. However, when a system involves nonpolynomial terms and many uncertainties, it is computationally challenging to solve PCE coefficients. In this work, the PCE and generalized dimension reduction methods were combined with the sampling-based gaussian quadrature rules to quickly calculate the PCE coefficients, and the efficiency of the algorithm was demonstrated with examples of biological systems. View this paper
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Modified Polynomial Chaos Expansion for Efficient Uncertainty Quantification in Biological Systems

  • Jeongeun Son,
  • Dongping Du and
  • Yuncheng Du
Appl. Mech.2020, 1(3), 153-173;https://doi.org/10.3390/applmech1030011

22 August 2020

Uncertainty quantification (UQ) is an important part of mathematical modeling and simulations, which quantifies the impact of parametric uncertainty on model predictions. This paper presents an efficient approach for polynomial chaos expansion (PCE)...

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Appl. Mech. - ISSN 2673-3161
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