
MONTE CARLO SIMULATIONS IN STATISTICAL PHYSICS
Wolfhard JANKE
Institut für Theoretische Physik, Universität Leipzig
Lectures:
 Introduction to Monte Carlo simulations
 Improved algorithms and generalised ensembles
 Applications to disordered systems
Contents: The aim of this lecture series is to give an overview on the current stateoftheart of Monte Carlo computer simulations and to illustrate them in the first two lectures with simple applications to the Ising model of statistical physics. After reviewing in the first lecture importance sampling Monte Carlo schemes based on Markov chains and standard local update rules such as the Metropolis and heatbath algorithm, statistical error analyses of simulation data and critical slowing down at a secondorder phase transition will be discussed. As an important tool for finitesize scaling analyses, histogram reweighting techniques are introduced.
Next advanced update algorithms will be considered which, for certain classes of models, can drastically improve the performance of simulations. This will be illustrated with clusterupdate algorithms, reducing critical slowing down at secondorder phase transitions, and multicanonical simulations, greatly improving simulations at firstorder phase transitions and, in general, for systems with rareevent states. A few other useful methods will be briefly mentioned.
Mainly intended as an outlook, the third lecture will be devoted to more advanced applications to disordered systems such as diluted ferromagnets, random lattices and spin glasses which in general require especially tailored algorithms for their successful simulation.
Focussing mainly on the basic concepts, the lecture series is addressed to a broad audience of students, whose main focus may range from applied to theoretical physics. Small exercises referring mainly to the first and partly the second lecture will be assigned, that should be worked out by the students.
Lecture I  Introduction to Monte Carlo simulations: This lecture introduces the basic concepts underlying Monte Carlo simulations and their statistical analysis. The power of the method will be illustrated for the Ising model.
 Importance sampling Monte Carlo
 Local update procedures (Metropolis, heatbath)
 Statistical error analyses (critical slowing down)
 Histogram reweighting techniques
 Applications to the Ising model
Lecture II  Improved algorithms and generalised ensembles: For certain classes of models the simulations can be drastically improved by using more advanced algorithms. This will be illustrated with clusterupdate algorithms, drastically reducing critical slowing down at secondorder phase transitions, and multicanonical simulations, greatly improving simulations at firstorder phase transitions and, in general, for systems with rareevent states. Other useful methods will be only briefly mentioned.
 Cluster algorithms
 Multigrid methods
 Generalized ensembles (multicanonical simulations etc.)
 Simulated and parallel tempering
Lecture III  Applications to disordered systems: Numerical simulations of quenched, disordered systems (e.g. randombond or diluted ferromagnets, random lattices or graphs, spin glasses) in general require especially tailored algorithms in order to achieve reliable results in reasonable computing times (which are usually still large, even on supercomputers). Methodological similarities to the problem of protein folding will be sketched. The objective of this lecture is to give an outlook to computer experiments for such systems and to illustrate them by specific examples without going too much into the details.
 Diluted ferromagnets
 Random lattices or graphs
 Spin glasses
 Protein folding
Literature:
Recent textbooks on the subject include:
 M.E.J. Newman and G.T. Barkema, Monte Carlo Methods in Statistical Physics (Clarendon Press, Oxford, 1999).
 D.P. Landau and K. Binder, Monte Carlo Simulations in Statistical Physics (Cambridge University Press, Cambridge, 2000).
 K. Binder and D.W. Heermann, Monte Carlo Simulations in Statistical Physics: An Introduction, 4th edition (Springer, Berlin, 2002).
 B.A. Berg, Markov Chain Monte Carlo Simulations and Their Statistical Analysis (World Scientific, Singapore, 2004).
A few review articles covering the material of the lectures are (see also http://www.physik.unileipzig.de/~janke/Ising_Lectures_Lviv.html):
 W. Janke, Nonlocal Monte Carlo Algorithms for Statistical Physics Applications, Mathematics and Computers in Simulations 47, 329 (1998).
 W. Janke, Statistical Analysis of Simulations: Data Correlations and Error Estimation, invited lecture notes, in: Proceedings of the Euro Winter School Quantum Simulations of Complex ManyBody Systems: From Theory to Algorithms, edited by J. Grotendorst, D. Marx, and A. Muramatsu, John von Neumann Institute for Computing, Jülich, NIC Series, Vol. 10, pp. 423445 (2002).
 W. Janke, Multicanonical Monte Carlo Simulations, Physica A254, 164 (1998).
 W. Janke, Histograms and All That, in: Computer Simulations of Surfaces and Interfaces, NATO Science Series, II. Mathematics, Physics and Chemistry  Vol. 114, Proceedings of the NATO Advanced Study Institute, Albena, Bulgaria, 9  20 September 2002, edited by B. Dünweg, D.P. Landau, and A.I. Milchev (Kluwer, Dordrecht, 2003), pp. 137157.
 W. Janke, FirstOrder Phase Transitions, in: Computer Simulations of Surfaces and Interfaces, NATO Science Series, II. Mathematics, Physics and Chemistry  Vol. 114, Proceedings of the NATO Advanced Study Institute, Albena, Bulgaria, 9  20 September 2002, edited by B. Dünweg, D.P. Landau, and A.I. Milchev (Kluwer, Dordrecht, 2003), pp. 111135.
 W. Janke, P.E. Berche, C. Chatelain, and B. Berche, Phase Transitions in Disordered Ferromagnets, in: NICSymposium 2004, Proceedings, edited by D. Wolf, G. Münster, and M. Kremer, John von Neumann Institute for Computing, Jülich, NIC Series, Vol. 20, pp. 241250 (2003).
 W. Janke, B.A. Berg, and A. Billoire, MultiOverlap Simulations of Spin Glasses, in: NIC Symposium 2001, Proceedings, edited by H. Rollnik and D. Wolf, John von Neumann Institute for Computing, Jülich, NIC Series, Vol. 9, pp. 301314 (2002).
 W. Janke and M. Weigel, Monte Carlo Studies of Connectivity Disorder,
in: High Performance Computing in Science and Engineering, Munich 2004, transactions of the Second Joint HLRB and KONWIHR Result and Reviewing Workshop, March 2nd and 3rd, 2004, Technical University of Munich (SpringerVerlag, Berlin, Heidelberg, New York, 2004), pp. 363373.
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