Introduction to the application of computational methods to models arising in science and engineering, concentrating mainly on the solution of partial differential equations. Topics include finite differences, finite element method, boundary value problems, numerical ODEs, and spectral methods.
This course covers classical methods in applied mathematics and math modeling. The topics include dimensional analysis, asymptotics, regular and singular perturbation theory, calculus of variation, random walks and the diffusion limit, classical solution techniques for PDE, common methods for parameter estimation like Maximum Likelihood Estimation (MLE), Maximum a Posteriori (MAP) estimation, Bayesian estimation. The techniques will be applied to models arising throughout the natural sciences.
Classical theory of ordinary differential equations and some of its modern developments in dynamical systems theory. Linear systems and exponential matrix solutions. Well-posedness for nonlinear systems. Qualitative theory: limit sets, invariant set and manifolds. Stability theory: linearization about an equilibrium, Lyapunov functions. Autonomous two-dimensional systems and other special systems. Prerequisites: advanced calculus, linear algebra and basic ODE.