See the general timetable for the dates and times of the various lectures.
4) Synchronization by common noise
We consider resetting a stochastic process by returning to the initial condition with a fixed rate. Resetting is a simple way of generating a nonequilibrium stationary state in the sense that the process is held away from any equilibrium state and a non-vanishing steady-state probability current is directed towards the resetting position. The nature and properties of nonequilibrium stationary state are questions of fundamental importance within statistical physics. Thus, the resetting paradigm provides a convenient framework within which to study such nonequilibrium properties.
Lecture I: Diffusion with Stochastic resetting
Lecture II: Many particle system
In the first lecture I will discuss the notion of hydrodynamic limit and as example we will present the proof for the symmetric simple exclusion process in contact with slow or fast reservoirs. In this process, particles jump in the bulk to one of its nearest neighbor sites with rate 1/2. At the boundaries particles are injected or removed at a rate which depends on a parameter that can be chosen in such a way that the boundary dynamics is either slow or fast. We will explore the hydrodynamic limit scenario and we will see a phase transition for the heat equation with different types of boundary conditions. This lecture is based on the article at arxiv.1407.7918.
In the third and last lecture we will analyze the model of the previous lecture, for a certain choice of the transition probability rate which has infinite variance and in the hydrodynamic limit we will get a fractional heat equation with Dirichlet boundary conditions written in terms of the regional fractional Laplacian.
I will discuss how noise can arise in deterministic systems with strong instability with respect to the initial conditions. Starting with a discussion of the Central limit theorem for deterministic systems with random initial conditions and trying to get to the derivation of stochastic processes as limit theorems.
Lesson 1 - Chaos and randomness
Lesson 2 - Gaussian or not Gaussian
Lesson 3 - Non-equilibrium
The lectures will discuss the following topics:
When a large deviation result is proved there is some topology involved in the statement because it affects the class of sets for which the estimates hold. Often the choice is natural and obvious. There may be a stronger topology in which the theory holds. This requires additional work and we may or may not be inclined to do it. However the application we have in mind might require it, in which case we have no choice. It is also possible that the the principle fails in the familiar strong topology and the weak topology is not sufficient to prove the result. Then we have to invent a new topology. We will look at some examples to illustrate these points.
Laurent Desvillettes (Univ. Paris Diderot) [videos: first lecture]
Chemical reaction networks appear in many industrial devices and natural processes (combustion, photosynthesis, etc.). When the spatial structure is not taken into account, they give rise to complex system of ODEs with polynomial terms, and a lot of results were obtained lately in the study of the large time behavior for the solutions of those systems.
When the chemical species are supposed to be traces in a solvent, they are diffusing (each with its diffusion rate), so that their concentrations are solution to a system of reaction-diffusion with polynomial reaction rates. The specificity of such systems is, at least for a large class of them (called systems with balanced equilibria) the existence of a Lyapunov functional related to the physical entropy.
Recently, the systems of reaction-diffusion coming out of chemical networks with balanced equilibria have attracted a lot of attention. The study of existence of weak and strong solutions to those systems uses a lot of subtle tools of analysis: renormalized solutions, duality methods, etc. Their large time behavior is also an interesting issue. It is possible to develop entropy methods and convexity arguments which enable to show convergence to equilibrium for some systems, but many interesting systems are still puzzling.
In this series of lectures, we shall present (using specific examples) the main features of reaction-diffusion systems related to chemical networks, and explain how to get typical existence results as well as large time behavior results.
Stefano Olla (doctoral course)
This course is an elementary introduction to Thermodynamics and Statistical Mechanics. The aim is to explain the main ideas in few concrete models, more than constructing a general theory.
In the first part of the course, the `principles of thermodynamics' are introduced, and it is explained how these principles can be deduced from the microscopic dynamics of molecules through space-time macroscopic limits. Thermodynamics is obtained as macroscopic theory, i.e., valid on macroscopic space-time scales, while statistical mechanics provides the microscopic model. This means that the objects of thermodynamics are those macroscopic complex systems that satisfy the thermodynamic principles, while statistical mechanics explains how this complexity arise from dynamics of systems with a very large number of components. The central point of this connection is the identification of the thermodynamic entropy, a function of the thermodynamic macroscopic equilibrium states, introduced by Clausius using Carnot cycles, with the Boltzmann definition of entropy in statistical mechanics, as logarithm of the number of microstates corresponding to the given macroscopic state. Boltzmann and Planck discovered this identification at the end of 19th century, but in order to understand it, we need to obtain the thermodynamic transformations constituting the Carnot cycle, isothermal and adiabatic, from the microscopic dynamics through a scaling limit procedure. This aspect differentiates this course from more classical courses in statistical physics, where only the equilibrium properties of the systems are studied.
As classical thermodynamics concerns transformations from an equilibrium state to another, in the second part of the course I will illustrate how some ideas generalize to transitions between non-equilibrium stationary states.