This chapter begins the final section of the book, which presents both review and new results of original research on decoherence and measurement theory. In this chapter, it is shown that normal quantum mechanics can lead to irreversible behavior in an open system, in contrast to the expectation of the Poincaré theorem that predicts repeating, cyclical behavior for all closed systems. The quantum Boltzmann equation, which implies the famous H-theorem that underlies all statistical mechanics, is derived.

Quantum field theory (QFT) provides us with one and almost only suitable language (or mathematical tool) for describing not only the motion and interaction of particles but also their “annihilation” and “creation” out of a field considered a priori in a sophisticated way, whose view seems to be suited for describing dislocations, as a particle or a string embedded within a crystalline ordered field. This chapter concisely overviews the method of QFT, emphasizing distinction from the quantum mechanics, conventionally used for a single and/or many particle problems, and its equivalence to the statistical mechanics. The alternative formalism based on Feynman path integral and its imaginary time representation are reviewed, as the foundation for our use in Chapter 10.

Exact solutions for infinite Ising systems are rare, specific in terms of the interactions allowed, and limited to one and two dimensions. To study a wider range of models we must resort to various approximation techniques. One of the simplest and most comprehensive of these is the mean-field approximation, the subject of this chapter. Some versions of this approximation rely on a self-consistent requirement, and in this respect the mean-field method for the Ising model is similar to a number of other self-consistent approximation methods in physics, including the Hartree–Fock approximation for atomic and molecular orbitals, the BCS theory of superconductivity, and the relaxation method for determining electric potentials. We will also introduce a somewhat different mean-field approach, the Landau–Ginzburg approximation, which is based on a series expansion of the free energy. One of the drawbacks of all of the mean-field theories, however, is that they predict the same mean-field critical exponents, which, unfortunately, are at odds with the results of exact solutions and experiments.

A useful way to solve a complex problem – whether in physics, mathematics, or life in general – is to break it down into smaller pieces that can be handled more easily. This is especially true of the Ising model. In this chapter, we investigate various partial-summation techniques in which a subset of Ising spins is summed over to produce new, effective couplings among the remaining spins. These methods are useful in their own right and are even more important when used as a part of position-space renormalization-group techniques.

In the chapters so far, we have studied a number of exact methods of calculation for Ising models. These studies culminated in the exact solution for an infinite one-dimensional Ising model, as well as the corresponding solution on a 2 × ∞ lattice. Neither of these systems shows a phase transition, however. In this chapter, we start with Onsager’s exact solution for the two-dimensional lattice, which quite famously does have a phase transition. Next, we explore exact series expansions from low and high temperature, and show how these results can be combined, via the concept of duality, to give the exact location of the phase transition in two dimensions.

In Chapter 3 we explored transformations where a finite group of Ising spins is summed to produce effective interactions among the remaining spins. In all of these cases a finite sum of Boltzmann factors is sufficient to solve the problem. We turn now to infinite systems, where a straightforward, brute-force summation is not possible. Instead, we develop a number of new techniques that allow us to evaluate an infinite summation in full detail.

In this chapter, we explore Ising systems that consist of just one or a few spins. We define a Hamiltonian for each system and then carry out straightforward summations over all the spin states to obtain the partition function. No phase transitions occur in these systems – in fact, an infinite system is needed to produce the singularities that characterize phase transitions. Even so, our study of finite systems yields a number of results and insights that are important to the study of infinite systems.

Few models in theoretical physics have been studied for as long, or in as much detail, as the Ising model. It’s the simplest model to display a nontrivial phase transition, and as such it plays a unique role in theoretical physics. In addition, the Ising model can be applied to a wide range of physical systems, from magnets and binary liquid mixtures, to adsorbed monolayers and superfluids, to name just a few. In this chapter, we present some of the background material that sets the stage for a detailed study of the Ising model in the chapters to come.

Anti-evolutionists sometimes use principles of thermodynamics in making their case. Though thermodynamics is normally considered a branch of physics, it has a strongly mathematical character that justifies its inclusion in this book. We discuss the basics of thermodynamics and statistical mechanics, and then explain why the anti-evolutionist version is such a caricature.

Chapter 2 introduces the statistical physics description of the rheology of concentrated colloidal suspensions at low Reynolds number. While the solvent is a Newtonian fluid, the suspension exhibits viscoelasticity and non-Newtonian rheology. After explaining the hydrodynamic interactions between Brownian particles mediated by the intervening solvent flow, two theoretical methods for describing the suspension rheology are discussed. In the Langevin Equation method, stochastic particle trajectories are considered under the influence of direct and hydrodynamic interactions, and solvent-induced fluctuating forces. This method is fundamental to simulation schemes where the macroscopic suspension stress is calculated from time-averaging the microscopic stress over representative trajectories. The main focus is on the second so-called generalized Smoluchowski equation (GSmE) method invoking a many-particles diffusion-advection equation for the configurational probability density of Brownian particles in shear flow. Based on the GSmE, real-space and Fourier-space schemes are discussed for calculating rheological properties including the shear stress relaxation function and steady state and dynamic viscosities. Starting from exact Green-Kubo relations for the shear stress relaxation function, the linear mode coupling theory (MCT) and its non-linear extension, termed Integration Through Transients (ITT), are introduced as versatile Fourier-space schemes. They allow for studying the rheology of concentrated suspensions close to glass and gel transitions.

Chapter 7 delves into a handful of combinatorial problems in flat origami theory that are more general than the single-vertex problems considered in Chapter 5. First, we count the number of locally-valid mountain-valley assignments of certain origami tessellations, like the square twist and Miura-ori tessellations. Then the stamp-folding problem is discussed, where the crease pattern is a grid of squares and we want to fold them into a one-stamp pile in as many ways as possible.Then the tethered membrane model of polymer folding is considered from soft-matter physics, which translates into origami as counting the number of flat-foldable crease patterns that can be made as a subset of edges from the regular triangle lattice.Many of these problems establish connections between flat foldings and graph colorings and statistical mechanics.