Cyclic Thermodynamic Processes and Entropy Production

Maximum Entropy modelling of a few protein families with pairwise interaction Potts models give values of σ ranging between 1.2 (when all amino acids present in the multiple

In an essential and quite general setup, based on networks, we identify Schnakenberg’s observables as the constraints that prevent a system from relaxing to equilibrium, showing

13: Configurational entropy for PVAc as a function of temperature for the experiment 3 with 60 h of aging such as described in table I, obtained by integration over temperature

After having investigated basic properties of generalised Lukasiewicz languages we first calculate their Bernoulli measures in Section 3.. Here we derive and in- vestigate in detail

This chapter is an introduction to entropy (or Lyapunov) methods for general (possibly nonlin- ear) dynamical system and some applications to some evolution PDEs (mostly

We may apply Theorem 2.14: the L 2 (G −1/2 )-norm is a Lyapunov functional and any solution converges with exponential rate to the associated equilibrium (uniquely defined thanks to

Villani, Sharp entropy dissipation bounds and explicit rate of trend to equilibrium for the spatially homogeneous Boltzmann equation, Preprint. [Tr,

In this section we focus on a translation invariant jump kernel J and identify the evolution equation generated by the associated non-local operator as the gradient flow of the