Author Archive CeMEAI

The meeting aims at discussing advances in different topics of Dynamical System ranging from randomness to bifurcations aspects. Renowned researchers will give lectures on their expertise addressed to students and professors who are interested in the subject.

Date and Venue

Invited Speakers

Mini courses

Schedule

Click on the speaker’s name below to see more details

The Auditorium’s locations at the ICMC building are shown below. Note that the scientific program above displays different colors in respect to the Auditorium selected for the day.

(Adapted from RECEPÇÃO 2016)

Dinner

The dinner will be held at Espaço Múltiplo

The location and route to the bar is shown at the Additional information below (inside the google maps you will be able to visualize the route).

Participation

How to apply

There is available poster presentation:

• • 1. Posters: Occurring simultaneously to the Coffee Break. It should be indicated the title of poster in the form below.

Limited financial support may be available for students, which must be indicated in the form.

Additional information

Restaurants and hotels at the ICMC surroundings

Organizers

Support

Greg Pavliotis

Abstract:

Sampling from a probability distribution in a high dimensional spaces is a standard problem in computational statistical mechanics, Bayesian statistics and other applications. A standard approach for doing this is by constructing an appropriate Markov process that is ergodic with respect to the measure from which we wish to sample. In this talk we will present a class of sampling schemes based on Langevin-type stochastic differential equations. We will show, in particular, nonreversible Langevin samplers, i.e. stochastic dynamics that do not satisfy detailed balance, have, in general, better properties than their reversible counterparts, in the sense of accelerating convergence to equilibrium and of reducing the asymptotic variance. Numerical schemes for such nonreversible samplers will be discussed and the connection with nonequilibrium statistical mechanics will be made.

Hayriye Cagnan

Abstract:

Our everyday actions, from decision making to motor control, are thought to involve information exchange through transient neural synchrony across multiple brain regions. Emerging evidence suggests that a range of neurological disorders such as Parkinson’s disease and essential tremor could be attributed to dysfunction of this fundamental neural property. Deep brain stimulation is commonly used to reduce disease symptoms by delivering high frequency electrical pulses to key brain regions. A new stimulation strategy, called phase-specific deep brain stimulation, aims to selectively regulate neural synchrony. I will highlight some experimental results which show the effect of phase-specific deep brain stimulation for the treatment of patients with pathological tremor and discuss how dynamic causal modelling could be used to infer effect of stimulation on disease circuit.

Jeroen Lamb

Abstract:

Despite the broad interest in probabilistic processes in  applied mathematics and the popularity of including “noise” or uncertainty as a modelling assumption, our understanding of the dynamics of random  systems is predominantly statistical and static, revealing little or  nothing about the actual “random dynamics”. In this talk I will discuss this assertion and give examples of open question and a few partial answers.

Matteo Tanzi

Abstract:

Networks of interacting systems with heterogeneous degrees and community structures are ubiquitously found in natural and artificial systems such as, among others, neuronal networks, gene-regulatory networks, and power grids. Recovering the characteristics of the networks and of the dynamics from observations of the systems is a crucial problem that has attracted a lot of attention. In this talk, I will present an approach to reconstruct the statistical properties of the connectivity structure of the network when the interaction strength between units is weak and the isolated dynamics of the nodes is chaotic. With relatively short time-series, our method can recover the degree distribution, the community structures, and build a model for the isolated dynamics and the interactions. The approach combines machine learning techniques with recent results on the ergodic theory of high-dimensional coupled systems. The procedure is able to accurately reconstruct the characteristic of simulated dynamics when tested on Scale-Free networks and graphs presenting Rich-Club motifs. We then used the results to predict the transitions to collective behaviour of the system from a single multivariate timeseries, observed in a state far from the transition.