ענבר סרוסי (אונ' תל-אביב)
יום שלישי, 26.3.2019, 11:30
חדר 337, בניין טאוב למדעי המחשב
Unravelling underlying complex structures from limited resolution measurements is a known problem arising in many scientific disciplines. We study a stochastic dynamical model with a multiplicative noise. It consists of a stochastic differential equation living on a graph, similar to approaches used in population dynamics or directed polymers in random media. This model has numerous applications, such as population growth, economic growth, and optimal control. We present a new application of this model in the context of Magnetic Resonance (MR) measurements of porous structure such as brain tissue from a single voxel. We develop a new tool for approximation of correlation functions based on spectral analysis that does not require translation invariance. This enables us to go beyond lattices and analyze general networks. We show, analytically, that this general model has different phases depending on the topology of the network. One of the main parameters which describe the network topology is the spectral dimension d ̃. We show that the correlation functions depend on the spectral dimension and that only for d ̃> 2 a dynamical phase transition occurs. We show by simulation how the system behaves for different network topologies, by defining and calculating the Lyapunov exponents on the graph.
PhD student under supervision of Prof. Nir Sochen.