Probabilistic Construction and Numerical Analysis of Model Verification and Validation

Speaker:Alireza Doostan,Ph.D.
John Hopkins University
Department of Civil EngineeringAbstract: In this presentation, some recent developments in verification and validation (V&V) of predictive models are introduced. Verification is a mathematical concept which aims at assessing the accuracy of the solution of a given computational simulation compare to sufficiently accurate or analytical solutions. Validation, on the other hand, is a physics-based issue that aims at appraising the accuracy of a computational simulation compare to experimental data.The proposed developments cast V&V in the form of an approximation-theoretic representation that permits their clear mathematical definition and resolution. In particular, three types of problems will be addressed. First, a-priori and a-posteriori error analysis of spectral stochastic Galerkin schemes, a widely used tool for uncertainty propagation, are discussed. Second, a statistical procedure is developed in order to calibrate the uncertainty associated with parameters of a predictive model from experimental or model-based measurements. An important feature of such data-driven characterization algorithm, is in its ability to simultaneously represent both the intrinsic uncertainty and also the uncertainty due to data limitation.
Third, a stochastic model reduction technique is proposed in order to increase the computational efficiency of spectral stochastic Galerkin schemes for the solution of complex stochastic systems.While the second part of this research is essential in model validation phase, the first part is particularly important as it provides one with basic components of the verification phase.

Location: Kaprielian Hall (KAP) - 203

Audiences: Everyone Is Invited

Contact: Evangeline Reyes