Handbook

Stochastic differential equation models play a prominent role in a range of application areas, including biology, chemistry, epidemiology, mechanics, microelectronics, economics, and finance. This course covers the theory and applications of stochastic differential equations, the design and implementation on computers of numerical methods for solving these practical mathematical equations. The course will start with a background knowledge of random variables, Brownian motion, and Ornstein-Uhlenbeck process. Other topics studied include: stochastic integrals, the Euler-Maruyama method, Milstein, higher order method, and stability and convergence. In addition to lectures, the distinct tutorial and lab classes will give students the opportunity to work on both mathematical exercises and computing problems.