Search arXivSearch

arXiv · 0901.2496

Four lectures on computational statistical physics

Abstract

In my lectures at the Les Houches Summer School 2008, I discussed central concepts of computational statistical physics, which I felt would be accessible to the very cross-cultural audience at the school. I started with a discussion of sampling, which lies at the heart of the Monte Carlo approach. I specially emphasized the concept of perfect sampling, which offers a synthesis of the traditional direct and Markov-chain sampling approaches. The second lecture concerned classical hard-sphere systems, which illuminate the foundations of statistical mechanics, but also illustrate the curious difficulties that beset even the most recent simulations. I then moved on, in the third lecture, to quantum Monte Carlo methods, that underly much of the modern work in bosonic systems. Quantum Monte Carlo is an intricate subject. Yet one can discuss it in simplified settings (the single-particle free propagator, ideal bosons) and write direct-sampling algorithms for the two cases in two or three dozen lines of code only. These idealized algorithms illustrate many of the crucial ideas in the field. The fourth lecture attempted to illustrate aspects of the unity of physics as realized in the Ising model simulations of recent years. More details on what I discussed in Les Houches, and wrote up (and somewhat rearranged) here, can be found in my book, "Statistical Mechanics: Algorithms and Computations", as well as in recent papers. Computer programs are available for download and perusal at the book's web site www.smac.lps.ens.fr.

Explore related subjects

Keep this discovery

BibTeXRIS

Werner Krauth. 2009-01-16. Four lectures on computational statistical physics. https://arxiv.org/abs/0901.2496

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Universal sampling of spin systems across quenched disorder

Statistical physics extracts macroscopic laws by averaging over the many microscopic degrees of freedom of a system. Disordered systems demand a second and far harder average, one over the quenched randomness itself. The classic analytical routes, the replica and cavity methods, become uncontrolled outside mean-field or tree-like limits, and conventional numerical algorithms like parallel tempering require expensive, independent equilibration for every disorder realization. In this work, we introduce a universal neural variational framework that amortizes inference across the disorder ensemble, eliminating both the need for per-instance Markov chain equilibration and the cost of retraining instance-specific variational ansatzes. Built on an encoder-decoder Transformer architecture, after training once, it produces an explicit approximation to the Boltzmann distribution given previously unseen disorder realizations without further optimization. We validate this framework on 2D Edwards-Anderson models, and apply it to the random-bond Ising model, successfully capturing the Binder cumulant crossings near the Nishimori multicritical point. These results shift the object of variational inference from the single instance to the disorder ensemble, opening a route to frustrated many-body systems where instance-by-instance computation is prohibitive.

cond-mat.stat-mech

Information-Theoretic Characterization of Macroscopic Chaos Emerging from the Chemical Master Equation

Open chemical reaction networks exhibit stochastic concentration dynamics at finite system sizes, whereas their macroscopic limit is governed by deterministic rate equations that can display chaos. In this Letter, we show theoretically that a rate of information loss constructed from two-time mutual information recovers the Kolmogorov-Sinai entropy in the deterministic limit. We verify this result through numerical simulations of a Markov jump process for a three-species system involving seven reactions.

cond-mat.stat-mech

Orientational order on non-orientable domains

We study the statistical properties of passive and active many-body systems with orientational degrees of freedom on non-orientable domains. By rephrasing topological constraints as non-local symmetry relations on an orientable double-cover, we show that non-orientability eliminates global rotational soft modes without acting like an external field. In a passive XY model, this results in topological caging, where orientational fluctuations that exhibit conventional diffusive behavior on a torus saturate on a Klein bottle to a finite value that we compute exactly in the thermodynamic limit. In models of active self-propelled particles with orientational degrees of freedom, topological caging persists despite continuously changing interaction neighborhoods. In an active Ising spin model, non-orientability enforces the coexistence of ordered anti-parallel domains with vanishing global polar order, a state that is absent on orientable domains.

cond-mat.stat-mech