Mechanics and Computation / View source

for Sensitivity Analysis, Uncertainty Quantification and Multiscale Modeling of High-Dimensional Environmental and Biological Systems

=Guang Lin, Ph.D.=
===September 29, 2012===

"Sensitivity Analysis, Uncertainty Quantification and Multiscale Modeling of High-Dimensional Environmental and Biological Systems"

Experience suggests that uncertainties often play an important role in quantifying the performance of complex systems. Therefore, uncertainty needs to be treated as a core element in modeling, simulation and optimization of complex systems. In this talk, a new formulation for quantifying uncertainty will be discussed. An integrated simulation framework will be presented that quantifies both numerical and modeling errors in an effort to establish "error bars" in CFD. In particular, stochastic formulations based on Galerkin and collocation versions of the generalized Polynomial Chaos (gPC) will be discussed. Additionally, we will present some effective new ways of dealing with this "curse-of-dimensionality". Particularly, adaptive ANOVA decomposition will be discussed in some detail. Several specific examples on random roughness problem, sensitivity analysis of the reaction rates on thrombin production using blood coagulation model, and parameter estimation in both subsurface porous media and climate models will be presented to illustrate the main idea of our approach.

In many microfluidic and biomedical applications there is often a need to model accurately multiscale flow phenomena across several orders of magnitude in spatiotemporal scale. Multiple scale models both in time and space can overcome this difficulty and provide a unified description of liquid flows from nanoscales to larger scales. Several concurrent coupling approaches will be introduced. An example of the proposed coupled approaches would be atomistic dynamics, Dissipative Particle Dynamics (DPD) and the incompressible Navier-Stokes equations, to cover a broad range of spatiotemporal scales starting from molecular to mesoscopic and to continuum for fluid flow. Additionally, a concurrent approach by coupling kinetic Monte Carlo with continuum model will be presented to model gas flow with complex surface catalytic reactions. Finally, a DPD-based multiscale model is introduced to model the dynamics and the deformation of red blood cell in blood flows. Numerical tests show good agreement with experiments.

Return to Sensitivity Analysis, Uncertainty Quantification and Multiscale Modeling of High-Dimensional Environmental and Biological Systems.

Retrieved from "http://mc.stanford.edu/Sensitivity_Analysis,_Uncertainty_Quantification_and_Multiscale_Modeling_of_High-Dimensional_Environmental_and_Biological_Systems"