This course covers a generalization of the classical differential- and integral calculus using Brownian motion. With this, Ito calculus stochastic differential equations can be formulated and solved, numerically and in some cases analytically. This yields a powerful tool for describing and simulating random phenomena in science, engineering and economics. The course starts with a necessary background in probability theory and Brownian motion. Then the Ito integral and the fundamental theorem of Ito calculus, Ito’s lemma, are introduced. Furthermore, numerical and analytical methods for the solution of stochastic differential equations are considered. The connections between stochastic differential equations and partial differential equations are investigated (the Feynman-Kac formula, the Fokker-Planck equation). Some applications of stochastic differential equations are presented. Mandatory computer assignments are included.
In a degree, this course may not be included together with another course with a similar content. If unsure, students should ask the Director of Studies in Mathematics and Mathematical Statistics. The course can also be included in the subject area of computational science and engineering.