Hierarchical Bayesian uncertainty quantification of Finite Element models using modal statistical information

Bayesian hierarchical statistical SIRS models

The classic SIR (susceptible-infectious-recovered) model, has been used extensively to study the dynamical evolution of an infectious disease in a large population. The SIR-susceptible (SIRS) model is an extension of the SIR model to allow modeling imperfect immunity (those who have recovered might become susceptible again). SIR(S) models assume observed counts are “mass balanced.” Here, mass b...

Interval Uncertainty Quantification in Numerical Models using Dynamic Fuzzy Finite Element Analysis

Uncertainty and variability are always to some extent present in numerical simulations. Especially in the model definition phase, lack of information, scatter in material properties or environmental conditions imply that a designer should incorporate the possible effects of this non-determinism into the design procedure. This can be achieved by incorporation of the non-determinism in the numeri...

investigation of apple vibration characteristics using finite element modal analysis

the most important quality indicator of fruits is the flesh firmness which is well correlated to their young’s modulus. in this research variation of vibration characteristics (shape modes, natural frequency) of apple due to change of material characteristics (density, young's models, poisson ratio) and apple volume was investigated using finite element simulation. an image processing technique...

Statistical Adjustment, Calibration, and Uncertainty Quantification of Complex Computer Models

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...