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Mastering Monte Carlo Methods: Scientific Computing Workshop

Join us for a comprehensive workshop on Monte Carlo methods, covering probability, random number generation, numerical integration, and advanced topics like Markov chains and quantum Monte Carlo methods. Learn about Metropolis algorithms, convergence analysis, cluster algorithms, extended ensemble methods, and more. Engage in practical sessions and discussions on non-equilibrium dynamics and statistical applications. Explore the characteristics and applications of Monte Carlo simulations.

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Mastering Monte Carlo Methods: Scientific Computing Workshop

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  1. Monte Carlo Methods in Scientific Computing 3-7 November 2003 Beijing International Center for Computational Physics

  2. Outline (Monday) • What is Monte Carlo • Introduction to probability • Random number generator • Numerical integration • Quasi-Monte Carlo method • Markov chain

  3. Outline (Tuesday) • Metropolis and other algorithms • Selected applications • Convergence and Monte Carlo error • Quantum Monte Carlo methods • Variational, diffusion Monte Carlo • Trotter-Suzuki formula

  4. Outline (Wednesday) • Cluster algorithms • Re-weighting methods • Extended ensemble methods (Multi-canonical, simulated tempering, replica MC, replica exchange) • Transition matrix MC, flat-histogram and Wang-Landau

  5. Thursday • Morning: Non-equilibrium dynamics in statistical mechanics and other applications (by Bo Zheng) • Afternoon: Markov chain Monte Carlo in statistics (by Junni Zhang)

  6. Friday Whole day: Monte Carlo method and its characteristics (by Pei Lucheng)

  7. Reference Books • M H Kalos and P A Whitlock, “Monte Carlo Methods”, John Wiley & Sons, 2nd ed, 2008. • D P Landau and K Binder, “A Guide to Monte Carlo Simulations in Statistical Physics”, 4th ed, Cambridge, 2015. • J S Liu, “Monte Carlo Strategies in Scientific Computing”, Springer,2002.

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