Hierarchical multiscale quantification of material uncertainty

Bayesian Uncertainty Quantification for Subsurface Inversion Using a Multiscale Hierarchical Model

Bayesian Uncertainty Quantification for Subsurface Inversion Using a Multiscale Hierarchical Model Anirban Mondal, Bani Mallick, Yalchin Efendiev & Akhil Datta-Gupta a Department of Statistics, Texas A&M University, College Station, TX 77843 (; ) b Department of Mathematics, Texas A&M University, College Station, TX 77843 () c Petroleum Engineering Department, Texas A&M University, College Stat...

An Efficient Computational Framework for Uncertainty Quantification in Multiscale Systems

Advances Concerning Multiscale Methods and Uncertainty Quantification in EXA-DUNE

In this contribution we present advances concerning efficient parallel multiscale methods and uncertainty quantification that have been obtained in the frame of the DFG priority program 1648 Software for Exascale Computing (SPPEXA) within the funded project EXA-DUNE. This project aims at the development of flexible but nevertheless hardware-specific software components and scalable high-level a...

Multiscale Surrogate Modeling and Uncertainty Quantification for Periodic Composite Structures

Computational modeling of the structural behavior of continuous fiber composite materials often takes into account the periodicity of the underlying micro-structure. A well established method dealing with the structural behavior of periodic micro-structures is the socalled Asymptotic Expansion Homogenization (AEH). By considering a periodic perturbation of the material displacement, scale bridg...

Multiscale Uncertainty Quantification based on A Generalized Hidden Markov Model

Variability is inherent randomness in systems, whereas incertitude is due to lack of knowledge. In this paper, a generalized hidden Markov model (GHMM) is proposed to quantify aleatory and epistemic uncertainties simultaneously in multiscale system analysis. The GHMM is based on a new imprecise probability theory that has the form of generalized interval. The new interval probability resembles ...