Title: Method of Distributions for Hyperbolic Conservation Laws with Random Inputs
Speaker: Daniel Tartakovsky, Stanford University
Abstract: Parametric uncertainty, considered broadly to include uncertainty in system parameters and driving forces (source terms and initial and boundary conditions), is ubiquitous in mathematical modeling. The method of distributions, which comprises PDF and CDF methods, quantifies parametric uncertainty by deriving deterministic equations for either probability density function (PDF) or cumulative distribution function (CDF) of model outputs. Since it does not rely on finite-term approximations (e.g., a truncated Karhunen-Loeve transformation) of random parameter fields, the method of distributions does not suffer from the "curse of dimensionality''. On the contrary, it is exact for a class of nonlinear hyperbolic equations whose coefficients lack spatio-temporal correlation, i.e., exhibit an infinite number of random dimensions. In settings that require a closure approximation, we use neural networks to learn the coefficients in the CDF equations from a training set of Monte Carlo runs.
Seminar URL: https://people.math.osu.edu/xing.205/seminar.html
