If you are interested in learning more about applied math research from your fellow students, or you want a friendly and constructive environment to practice presenting your own applied math research, AMGSS is for you! This is an informational and sign-up meeting, so come to learn more about AMGSS and/or to sign up to present…

In this talk we develop a method for efficient computation of reduced-order nonlinear solutions (RONS). RONS is a framework to create reduced-order models for time-dependent partial differential equations (PDEs) where the reduced-order solution has nonlinear dependence on time-varying parameters. With RONS we obtain an explicit set of ordinary differential equations (ODEs) to evolve the parameters.…

We consider sensitivity analysis of Bayesian inverse problems with respect to modeling uncertainties. To this end, we consider sensitivity analysis of the information gain, as measured by the Kullback-Leibler divergence from the posterior to the prior. This choice provides a principled approach that leverages key structures within the Bayesian inverse problem. Also, the information gain…