Enkeltfag Engelsk 5 ECTS

Diffusioner og stokastiske differentialligninger

Overordnede kursusmål

The course connects the theory of molecular diffusion with the theory of differential equations driven by noise. It enables the student to build and examine models of how noise and uncertainty propagates in dynamic systems. This can be used for time series analysis, and for dynamic decision making under uncertainty, i.e. stochastic control. The course contains theory for stochastic differential equations, stochastic calculus, and applications to engineering problems.

See course description in English

Define and describe diffusion and Brownian motion
Compute stochastic integrals, most importantly the Ito integral, and apply the rules of stochastic calculus
Build stochastic dynamic models by combining ordinary differential equations with models of how noise affects the system
Explain how a given stochastic differential equation corresponds to a certain advective-diffusive transport equation
Investigate the properties of a stochastic differential equation in terms of sample paths and transition probabilities, both analytically and numerically.
Implement a state estimation filter for analyzing time series data based on a stochastic differential equation
Identify the optimal strategy to control a system in presence of noise
Evaluate the importance of including noise in a study of a given system.

The course starts with advective and diffusive transport, and Monte Carlo simulation of a molecule in flow. We then turn to Brownian motion and stochastic integrals, and establish the Ito integral. We define stochastic differential equations (sde’s), and cover analytical and numerical techniques to solve them. We describe the transition probabilities of solutions to sde’s, and establish the forward and backward Kolmogorov equations, including boundary conditions/boundary behavior. We consider stochastic filtering in sde’s with discrete time measurements, stochastic stability, and stochastic control. The theory is illustrated with applications in engineering, physics, biology, and oceanography.

01035/02407/02417/02443/01617/01418, The course assumes some exposure to stochastic processes, for example obtained through 02407, 02417 or 02443. Familiarity with nonlinear dynamic systems, e.g., 01617. Some exposure to partial differential equations, e.g., 01418. Programming using R, python, Matlab, or similar is assumed.