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New methods in Monte Carlo simulations: parallel, adaptive, irreversible

Location : CECAM-HQ-EPFL, Lausanne, Switzerland
September 2, 2019 – September 4, 2019

The Monte Carlo method is well established and broadly used in a wide range of disciplines. The Metropolis-Hastings algorithm provides a nearly universal solution to the problem of importance sampling. Cluster algorithms have been devised to beat critical slowing down at continuous phase transitions, multicanonical simulations allow to study phase coexistence at first-order transitions. These and further successes notwithstanding, significant challenges remain and the field has continued to develop in a dynamic fashion.  Recent exciting advances in simulation methods include improvements to the Markov chain method as the workhorse of Monte Carlo simulations, such as event-chain techniques, fast methods for treating systems with long-range interactions, simulations replacing the diffusive motion implied by detailed balance with a semi-ballistic movement achieved through lifting, and perfect sampling ensured through coupling-from-the-past. Other approaches such as population annealing dispose with the concept of Markov chains altogether and achieve improved equilibration together with near perfect parallel scalability through the use of sequential Monte Carlo. This workshop will provide an overview of the state-of-the-art in Monte Carlo simulations in statistical physics, identify the open challenges in the field, and work towards their solution through intense discussions and exchange of ideas between researchers working on different strands of methods.


Youjin Deng, USTC, Hefei, China
Alexander Hartmann, University of Oldenburg, Germany
Koji Hukushima, University of Tokyo, Japan
Wolfhard Janke, University of Leipzig, Germany
Werner Krauth, ENS Paris, France
Faming Liang, Purdue University, West Lafayette, U.S.A.
Junwei Liu, HKUST, Hong Kong, China
Jon Machta, University of Massachussetts, Amherst, U.S.A.
Manon Michel, Ecole Polytechnique, Palaiseau, France
Lev Shchur, Landau Institute for Theoretical Physics, Chernogolovka, Russia
Simon Trebst, University of Cologne, Germany
Peter Virnau, Johannes Gutenberg University, Mainz, Germany
Wenlong Wang, KTH, Stockholm, Sweden
Martin Weigel, Coventry University, UK
David Wilson, University of Washington, Seattle, U.S.A.
Thomas  Wüst, ETH Zurich, Switzerland
Johannes Zierenberg, MPI-DS, Göttingen, Germany

Monday September 2nd 2019 – Day 1

09:15 to 09:30 – Welcome and Opening
Session I: Event-chain algorithms and non-reversible simulations (Chair: Jon Machta)

09:30 to 10:15 – Werner Krauth 
Irreversible Markov chains: adaptive factor fields, parallel implementations, and the JeLLyFysh open-source project
10:30 to 11:00 – Filip Uhlik 
Hybrid Monte Carlo simulations of polymers
11:15 to 11:45 – Coffee Break
11:45 to 12:30 – Michel Manon 
Non-reversibility in Monte Carlo methods through symmetry hunting
12:45 to 14:00 – Lunch
14:00 to 15:00 – Discussion
Session II: Generalized ensembles (Chair: Stefan Schnabel)

15:00 to 15:45 – Johannes Zierenberg 
Parallel multicanonical simulations and their application
16:00 to 16:30 – Bortolo Matteo Mognetti 
Sampling functionalized polymers forming reversible linkages using configurational bias algorithms
16:45 to 17:15 – Coffee Break
17:15 to 18:00 – Thomas Wüst 
Why do polymers knot and proteins (k)not? – or – Our need for more powerful MC methods even for the simplest protein models
18:30 to 20:00 – Welcome Reception
Tuesday September 3rd 2019 – Day 2
Session III: Population annealing and parallel tempering (Chair: Johannes Zierenberg)

09:00 to 09:45 – Koji Hukushima 
Application of population annealing to Bayesian statistics
10:00 to 10:45 – Jon Machta 
Population annealing simulations of glassy systems
11:00 to 11:30 – Coffee Break
11:30 to 12:15 – Wenlong Wang 
Population annealing: Recent developments and an application to spin glass
12:30 to 13:00 – Martin Weigel 
Sampling the density of states with population annealing
13:15 to 14:30 – Lunch
Session IV: Machine Learning (Chair: Alexander Hartmann)

14:30 to 15:15 – Junwei Liu 
Self-learning Monte Carlo with mean-field theory motivated structured self-attention network
15:30 to 16:00 – Miriam Klopotek 
Unsupervised machine learning of Monte-Carlo samples from statistical-mechanical equilibrium
16:15 to 16:45 – Coffee Break
16:45 to 17:30 – Faming Liang 
Extended stochastic gradient MCMC algorithms for large-scale Bayesian computing
17:45 to 18:45 – Moderated discussion
20:00 to 22:00 – Social Dinner
Wednesday September 4th 2019 – Day 3
Session V: Rare Events and Non-Equilibrium (Chair: Werner Krauth)

09:00 to 09:45 – Peter Virnau 
An improved subbox method for the determination of critical points and applications to active particles
10:00 to 10:30 – Henrik Christiansen 
Coarsening and aging in the long-range Ising model
10:45 to 11:15 – Coffee Break
11:15 to 12:00 – Alexander Hartmann 
I want it all and I want it now! Obtaining (almost) the full probability distribution for equilibrium and non-equilibrium stochastic problems using Monte Carlo simulations
12:15 to 13:30 – Lunch
Session VI: New Solutions to Old Problems (Chair: Manon Michel)

13:30 to 14:00 – Wolfhard Janke 
Accelerating molecular dynamics through populations
14:15 to 14:45 – Schnabel Stefan 
Simulation of a large polymer with untruncated interaction near the collapse
15:00 to 15:45 – Lev Shchur 
Acceptance ratios of local Monte Carlo updates
16:00 to 16:30 – Leaving Coffee

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