Georgia Institute of Technology College of Sciences

Dr. Hurth will talk about two relatively simple, related switching systems: one in 1D, the other in 2D. For both systems, he will sketch how to analyse the density of the associated invariant measure. This is straightforward for the 1D-example, but proves somewhat unexpectedly difficult for the 2D one.

We consider a class of dynamical systems with random switching with the following specifics: Given a finite collection of smooth vector fields on a finite-dimensional smooth manifold, we fix an initial vector field and a starting point on the manifold. We follow the solution trajectory to the corresponding initial-value problem for a random, exponentially distributed time until we switch to a new vector field chosen at random from the given collection. Again, we follow the trajectory induced by the new vector field for an exponential time until we make

Theoretical aspects: If a smooth dynamical system on a compact invariant set is structurally stable, then it has the shadowing property, that is, any pseudo (or approximate) orbit has a true orbit nearby. In fact, the system has the Lipschitz shadowing property, that is, the distance between the pseudo and true orbit is at most a constant multiple of the local error in the pseudo orbit. S. Pilyugin and S. Tikhomirov showed the converse of this statement for discrete dynamical systems, that is, if a discrete dynamical system has

Many complex models from science and engineering can be studied in the framework of coupled systems of differential equations on networks. A network is given by a directed graph. A local system is defined on each vertex, and directed edges represent couplings among vertex systems. Questions such as stability in the large, synchronization, and complexity in terms of dynamic clusters are of interest. A more recent approach is to investigate the connections between network topology and dynamical behaviours. I will present some recent results

The emergence of the 2009 H1N1 influenza A strain and delays in production of vaccine against it illustrate the importance of optimizing vaccine allocation. We have developed computational optimization models to determine optimal vaccination strategies with regard to multiple objective functions: e.g.~deaths, years of life lost, economic costs. Looking at single objectives, we have found that vaccinating children, who transmit most, is robustly selected as the optimal allocation. I will discuss ongoing extensions to this work to

Synchronization of coupled oscillators, such as grandfather clocks or metronomes, has been much studied using the approximation of strong damping in which case the dynamics of each reduces to a phase on a limit cycle. This gives rise to the famous Kuramoto model. In contrast, when the oscillators are Hamiltonian both the amplitude and phase of each oscillator are dynamically important. A model in which all-to-all coupling is assumed, called the Hamiltonian Mean Field (HMF) model, was introduced by Ruffo and his colleagues. As for the Kuramoto model, there

Probabilistic methods in dynamical systems is a popular area of research. The talk will present the origin of the interplay between both subjects with Poincar\'e's unpredictability and Kolmogorov's axiomatic treatment of probability, followed by two main breakthroughs in the 60es by Ornstein and Gordin. Present studies are concerned with two main problems: transferring probabilistic laws and laws for 'smooth' functions. Recent results for both type of questions are explained at the end.

I will present a KAM theorem on the existence of codimension-one invariant tori with Diophantine rotation vector for volume-preserving maps. This is an a posteriori result, stating that if there exists an approximately invariant torus that satisfies some non-degeneracy conditions, then there is a true invariant torus near the approximate one. Thus, the theorem can be applied to systems that are not close to integrable. The method of proof provides an efficient algorithm for

(Joint with: M. Benedicks and D. Schnellmann) Many interesting dynamical systems possess a unique SRB ("physical")measure, which behaves well with respect to Lebesgue measure. Given a smooth one-parameter family of dynamical systems f_t, is natural to ask whether the SRB measure depends smoothly on the parameter t. If the f_t are smooth hyperbolic diffeomorphisms (which are structurally stable), the SRB measure depends differentiably on the parameter t, and its derivative is given by a "linear response" formula (Ruelle, 1997).

A new approach based on Wasserstein distances, which are numerical costs ofan optimal transportation problem, allows to analyze nonlinear phenomena ina robust manner. The long-term behavior is reconstructed from time series, resulting in aprobability distribution over phase space. Each pair of probabilitydistributions is then assigned a numerical distance that quantifies thedifferences in their dynamical properties. From the totality of all these distances a low-dimensional representation ina Euclidean spaceis derived.