Statistical Framework for Uncertainty Quantification in Computational Molecular Modeling

An Efficient Computational Framework for Uncertainty Quantification in Multiscale Systems

Stochastic representations and statistical inverse identification for uncertainty quantification in computational mechanics

The paper deals with the statistical inverse problem for the identification of a nonGaussian tensor-valued random field in high stochastic dimension. Such a random field can represent the parameter of a boundary value problem (BVP). The available experimental data, which correspond to observations, can be partial and limited. A general methodology and some algorithms are presented including som...

Quantification of Uncertainty in Computational Fluid Dynamics

This review covers Verification, Validation, Confirmation and related subjects for computational fluid dynamics (CFD), including error taxonomies, error estimation and banding, convergence rates, surrogate estimators, nonlinear dynamics, and error estimation for grid adaptation vs Quantification of Uncertainty.

Uncertainty Quantification In Large Computational Engineering Models

While a wealth of experience in the development of uncertainty quantification methods and software tools exists at present, a cohesive software package utilizing massively parallel computing resources does not. The thrust of the work to be discussed herein is the development of such a toolkit, which has leveraged existing software frameworks (e.g., DAKOTA (Design Analysis Kit for OpTimizAtion))...

Uncertainty Quantification in Computational Fluid Dynamics

Bargaining with reading habit is no need. Reading is not kind of something sold that you can take or not. It is a thing that will change your life to life better. It is the thing that will give you many things around the world and this universe, in the real world and here after. As what will be given by this uncertainty quantification in computational fluid dynamics, how can you bargain with th...