Limiting theorems in statistical mechanics. The mean-field case.

The Curie-Weiss model is defined by ah Hamiltonian according to spins interact. For some particular values of the parameters, the sum of the spins normalized with square-root normalization converges or not toward Gaussian distribution. In the thesis we investigate some correlations between the behaviour of the sum and the central limit for interacting random variables.

Statistical Mechanics of Monomer-Dimer Models on Complete and Erdös-Rényi Graphs

Nella tesi sono trattate due famiglie di modelli meccanico statistici su vari grafi: i modelli di spin ferromagnetici (o di Ising) e i modelli di monomero-dimero. Il primo capitolo è dedicato principalmente allo studio del lavoro di Dembo e Montanari, in cui viene risolto il modello di Ising su grafi aleatori. Nel secondo capitolo vengono studiati i modelli di monomero-dimero, a partire dal lavoro di Heilemann e Lieb,con l'intento di dare contributi nuovi alla teoria. I principali temi trattati sono disuguaglianze di correlazione, soluzioni esatte su alcuni grafi ad albero e sul grafo completo, la concentrazione dell'energia libera intorno al proprio valor medio sul grafo aleatorio diluito di Erdös-Rényi.

Statistical mechanics of polymer models: rigorous results and applications

Monomer-dimer models are amongst the models in statistical mechanics which found application in many areas of science, ranging from biology to social sciences. This model describes a many-body system in which monoatomic and diatomic particles subject to hard-core interactions get deposited on a graph. In our work we provide an extension of this model to higher-order particles. The aim of our work is threefold: first we study the thermodynamic properties of the newly introduced model. We solve analytically some regular cases and find that, differently from the original, our extension admits phase transitions. Then we tackle the inverse problem, both from an analytical and numerical perspective. Finally we propose an application to aggregation phenomena in virtual messaging services.

Caratterizzazione della misura di entropia di singolo nodo nell'ambito della teoria statistica dei network

In questo lavoro si è affrontata la definizione e la caratterizzazione di una misura di entropia di singolo nodo nell’ambito della teoria statistica dei network, per ottenere informazioni a livello di singolo nodo a fini di analisi e classificazione. Sono state introdotte e studiate alcune proprietà di questi osservabili in quanto la Network Entropy, precedentemente definita e utilizzata nello stesso contesto, fornisce un’informazione globale a livello dell’intero network. I risultati delle analisi svolte con questa definizione sono stati confrontati con una seconda definizione di entropia di singolo nodo proveniente dalla letteratura, applicando entrambe le misure allo stesso problema di caratterizzazione di due classi di nodi all’interno di un network

Implementation of network entropy algorithms on hpc machines, with application to high-dimensional experimental data

Network Theory is a prolific and lively field, especially when it approaches Biology. New concepts from this theory find application in areas where extensive datasets are already available for analysis, without the need to invest money to collect them. The only tools that are necessary to accomplish an analysis are easily accessible: a computing machine and a good algorithm. As these two tools progress, thanks to technology advancement and human efforts, wider and wider datasets can be analysed. The aim of this paper is twofold. Firstly, to provide an overview of one of these concepts, which originates at the meeting point between Network Theory and Statistical Mechanics: the entropy of a network ensemble. This quantity has been described from different angles in the literature. Our approach tries to be a synthesis of the different points of view. The second part of the work is devoted to presenting a parallel algorithm that can evaluate this quantity over an extensive dataset. Eventually, the algorithm will also be used to analyse high-throughput data coming from biology.

Diagnosing criticality in symmetric and chiral clock models

Quantum clock models are statistical mechanical spin models which may be regarded as a sort of bridge between the one-dimensional quantum Ising model and the one-dimensional quantum XY model. This thesis aims to provide an exhaustive review of these models using both analytical and numerical techniques. We present some important duality transformations which allow us to recast clock models into different forms, involving for example parafermions and lattice gauge theories. Thus, the notion of topological order enters into the game opening new scenarios for possible applications, like topological quantum computing. The second part of this thesis is devoted to the numerical analysis of clock models. We explore their phase diagram under different setups, with and without chirality, starting with a transverse field and then adding a longitudinal field as well. The most important observables we take into account for diagnosing criticality are the energy gap, the magnetisation, the entanglement entropy and the correlation functions.

Transizioni di fase nell'allenamento delle Macchine di Boltzmann Ristrette

In this thesis, we dealt with Restricted Boltzmann Machines with binary priors as models of unsupervised learning, analyzing the role of the number of hidden neurons on the amount of examples needed for a successful training. We simulated a teacher-student scenario and calculated the efficiency of the machine under the assumption of replica symmetry to study the location of the critical threshold beyond which learning begins. Our results confirm the conjecture that, in the absence of correlation between the weights of the data-generating machine, the critical threshold does not depend on the number of hidden units (as long as it is finite) and thus on the complexity of the data. Instead, the presence of correlation significantly reduces the amount of examples needed for training. We have shown that this effect becomes more pronounced as the number of hidden units increases. The entire analysis is supported by numerical simulations that corroborate the results.

Numerical investigation of turbulent/non-turbulent interface

The subject of this work is the diffusion of turbulence in a non-turbulent flow. Such phenomenon can be found in almost every practical case of turbulent flow: all types of shear flows (wakes, jet, boundary layers) present some boundary between turbulence and the non-turbulent surround; all transients from a laminar flow to turbulence must account for turbulent diffusion; mixing of flows often involve the injection of a turbulent solution in a non-turbulent fluid. The mechanism of what Phillips defined as “the erosion by turbulence of the underlying non-turbulent flow”, is called entrainment. It is usually considered to operate on two scales with different mechanics. The small scale nibbling, which is the entrainment of fluid by viscous diffusion of turbulence, and the large scale engulfment, which entraps large volume of flow to be “digested” subsequently by viscous diffusion. The exact role of each of them in the overall entrainment rate is still not well understood, as it is the interplay between these two mechanics of diffusion. It is anyway accepted that the entrainment rate scales with large properties of the flow, while is not understood how the large scale inertial behavior can affect an intrinsically viscous phenomenon as diffusion of vorticity. In the present work we will address then the problem of turbulent diffusion through pseudo-spectral DNS simulations of the interface between a volume of decaying turbulence and quiescent flow. Such simulations will give us first hand measures of velocity, vorticity and strains fields at the interface; moreover the framework of unforced decaying turbulence will permit to study both spatial and temporal evolution of such fields. The analysis will evidence that for this kind of flows the overall production of enstrophy , i.e. the square of vorticity omega^2 , is dominated near the interface by the local inertial transport of “fresh vorticity” coming from the turbulent flow. Viscous diffusion instead plays a major role in enstrophy production in the outbound of the interface, where the nibbling process is dominant. The data from our simulation seems to confirm the theory of an inertially stirred viscous phenomenon proposed by others authors before and provides new data about the inertial diffusion of turbulence across the interface.

Statistical mapping of real environments with uwb radars

In this master thesis I evaluated the performance of a Ultra-Wide Bandwidth (UWB) radar system for indoor environments mapping. In particular, I used a statistical Bayesian approach which is able to combine all the measurements collected by the radar, including system non-idealities such as the error on the estimated antenna pointing direction or on the estimated radar position. First I verified through simulations that the system was able to provide a sufficiently accurate reconstruction of the surrounding environment despite the limitations imposed by the UWB technology. In fact, the emission of UWB pulses is limited in terms of transmitted power by international regulations. Motivated by the promising results obtained through simulations, I successively carried out a measurement campaign in a real indoor environment using a UWB commercial device. The obtained results showed that the UWB radar system is capable of providing an accurate reconstruction of indoor environments also adopting not directional antennas.