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From Werner KRAUTH

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Werner Krauth
Laboratoire de Physique
École normale supérieure
24 rue Lhomond
75005 Paris
France
werner.krauth@ens.fr


CNRS Research Director - Theoretical Physics (Directeur de recherche CNRS - classe exceptionnelle).

Visiting Professor in Physics - University of Oxford (2023 - 26).

Keeley Visiting Fellow - Wadham College (Oxford, UK) (October 2023 - March 2024).

Distinguished Visiting Professor, Simons Center for Computational Physical Chemistry, New York University, New York (NY, USA) - April, May 2024

From January to June 2018, I was the 2018 Martin-Gutzwiller fellow at the Max-Planck-Institute for the Physics of Complex Systems in Dresden (Germany).

In 2018, I was a recipient of the Humboldt research award (Alexander von Humboldt Foundation).

Contents

BegRohu Lectures 2024

My 2024 Lectures at the Beg Rohu Summer school run from 04 June 2024 through 14 June 2024. Click here for sample programs and other material

New York University (1 April - 31 May 2024)

In the abovementioned period, I've been Distinguished Visiting Professor at the Simons Center for Computational Physical Chemistry, where I've had an incredibly rich experience, with collaborations and visits at the Physics and Chemistry departments at New York University, the Center for Computational Mathematics at the Flatiron Institute (New York (NY)), D. E. Shaw Research, and Stony Brook University.


Oxford Lectures 2024

My 2024 Public Lectures at the University of Oxford (UK) run from 16 January 2024 through 5 March 2024. Click here for lecture notes and other material


Markov-chain Monte Carlo: A modern primer

This is the title of a Masterclass that I gave from 28 February through 3 March 2022 at the Faculty of Natural & Mathematical Sciences at King's College London (Great Britain). The Masterclass was organized into six separate lectures that treated everything from the foundations of MCMC (Markov-chain Monte Carlo) to modern lifted Markov chains, the concepts of perfect sampling (with practical applications), meta algorithms and, to wrap up: consensus sampling. The wonderful week at King's and in central London was my first physics trip since February 2020. While at King's College London, I also gave a talk Fast non-reversible Markov chains in statistical physics, that included some of our most recent work.


Workshop on ECMC and related subjects

An informal workshop on ECMC and related subjects took place on Tuesday 11 May 2021 8:30am - 12:30pm Paris time. 30 researchers participated, from the University of Dortmund (Germany), FAU-University of Erlangen (Germany), University of Bonn (Germany), University of Tokyo (Japan), Nagoya Institute of Technology (Japan), University Clermont-Auvergne (France), ESPCI Paris (France) and ENS Paris (France).

Join our group

Please check out my group page if you are interested in joining my group.

Fast irreversible Markov chains in statistical physics

This was the title of an invited plenary talk (slides) that I gave on September 10, 2019 at the CECAM50 conference celebrating the 50 years of this European Center for atomic and molecular computing.

Milestone Research

A paper, on a first-order transition in two dimensions, by a collaboration on three continents (!) that I published a few years ago in Physical Review E together with M. Engel, J. A. Anderson, S. C. Glotzer, M. Isobe, and E. P. Bernard, was chosen as the milestone article for 2013 by the journal's editorial board. This 2013 paper confirmed research published in 2011, in Physical Review Letters, with Etienne Bernard, on what really goes on in two-dimensional melting. See here for the story of the paper.

Video recordings of research talks

Hard-disk packings and two phase transitions of two-dimensional particle systems Invited talk at the workshop "Optimal Point Configurations on Manifolds", Erwin Schrödinger International Institute for Mathematics and Physics, University of Vienna, Vienna (Austria), 2021 (online talk)

Fast stochastic sampling with irreversible, totally asymmetric, Markov chains (Invited talk at Institute for Pure & Applied Mathematics, UCLA, Los Angeles (USA), 2017)

Current research

Go to Past Research Notices

I am deeply interested in statistical and condensed-matter physics, often in connection to computation and algorithms. Current interests are in hard spheres, mainly the melting transition in two-dimensional disks and in two-dimensional melting, bosons (in collaboration with the experimental groups at ENS), and the theory of convergence and of coupling in Markov chains. Recent work in my research group has led to the redefinition of the dominant Markov-chain Monte Carlo paradigm, namely the Metropolis algorithm. This has already allowed us to propose powerful algorithms for particle systems, continuous spin models and long-range systems, and to obtain important physical results. Research on the beyond-Metropolis paradigm, together with applications in classical and quantum physics and its interfaces will likely be a focus of my research activity in the next few years.

Fast, approximation-free molecular simulation of the SPC/Fw water model using non-reversible Markov chains

Many fields of computational science concern the sampling of configurations x from a distribution pi(x) which can often be written as pi(x) = exp[-beta E(x)] (that is, as a Boltzmann distribution). The configuration x then refers to the positions of thousands or millions of atoms with complicated, often long-ranged, interactions. Over the years, I have been interested in sampling methods which do not evaluate the energy E (or the difference of energies, or the gradient of E) in order to sample pi(x) = exp[-beta E(x)]. This is possible because of our use of the factorized Metropolis filter within the framework of the event-chain Monte Carlo algorithm. In July 2024, finally, the manuscript ["Fast, approximation-free molecular simulation ... "] on which I had worked together with Philipp Höllmer and A. C. Maggs was published in the Journal "Scientific Reports". It actually proves that one can simulate large water systems (in our case, the SPC/Fw water model) without any approximation. We generate millions of samples, but absolutely do not know what is the energy of our configurations.

The remarkable efficiency of our simulation method is rooted in three paradoxes. First, the Markov process is non-reversible (that is, effectively out-of-equilibrium), yet its steady state coincides with the equilibrium Boltzmann distribution. Second, the Boltzmann distribution exp(−βU) is sampled without any approximation and with great efficiency although the total potential U and its derivatives, the forces, are never evaluated. This sidesteps all the problems with limited-precision calculations of energies and forces. The third paradox is the bundling of O(N) independent decisions to interrupt the straight-line trajectory of the piecewise-deterministic Markov process into an expression that can be evaluated in constant time. The paper is openly accessible, and even all the computer code has been rendered open-source.

Fast, approximation-free molecular simulation of the SPC/Fw water model using non-reversible Markov chains

Lifted TASEP: a Bethe ansatz integrable paradigm for non-reversible Markov chains

In recent years, my colleagues and I have worked on a multitude of computational algorithms which improve on the classical methods. Specifically, we have worked on Monte Carlo algorithms based on non-reversible Markov chains. Such algorithms have had successes in applications but are generally difficult to analyze, resulting in a scarcity of exact results. In a recent manuscript Lifted TASEP: a Bethe ansatz integrable paradigm for non-reversible Markov chains with my colleague Fabian Essler (Oxford), we introduce the “lifted” TASEP (totally asymmetric simple exclusion process) as a paradigm for non-reversible Markov chains. Our model can be viewed as a second-generation lifting of the reversible Metropolis algorithm on a one-dimensional lattice and is exactly solvable by an unusual kind of coordinate Bethe ansatz. We establish the integrability of the model and present strong evidence that the lifting leads to relax- ation on shorter timescales than in the KPZ (Kardar–Parisi–Zhang) universality class.


Continue with Past Research Notices

Upcoming events

Here is the schedule of past events

Text book

Cover of a book I wrote in 2006 Here is the book's website
Cover of a book I wrote in 2006 Here is the book's website


Interview, Popular story, video conference

2012 interview at Ecole normale supérieure (in French)

CNRS special on our work on two-dimensional melting (June 2013) (in French) in Japanese (!)

2012 Conference on time's arrow (video, in French) in the framework of the Festival "acceleration" Sacre Doctoral school

Video presentation of the Massive Open Online course at ENS

Editorial "Coming home from a MOOC", about teaching a Massive Open Online Course (MOOC) (October 2014)

Grande conférence scientifique "Du déterminisme au stochastique : du hasard classique à l'aléatoire quantique", for the incoming science students at ENS (in French, September 2015)

"The largest Lecture Hall in the world", Article in "Physik Journal" on MOOCs, and in particular on my own MOOC (in German, March 2017)

A picture book of algorithms

Direct-sampling algorithm for ideal bosons in a trap (see article with M. Holzmann). Adapted for interacting bosons, this algorithm was used in a variety of articles.
Direct-sampling algorithm for ideal bosons in a trap (see article with M. Holzmann). Adapted for interacting bosons, this algorithm was used in a variety of articles.
Event-chain Monte Carlo algorithm for hard spheres and related systems (see article with E. P. Bernard and D. B. Wilson, including Python implementation). This (fantastic) algorithm, about two orders of magnitude faster than local Monte Carlo, was used in our discovery of the first-order liquid-hexatic phase transition in hard disks. The method can be generalized to continuous potentials, and we used it to map out the phase diagrams of soft-disk systems. Look here for an implementation of the event-chain algorithm
Event-chain Monte Carlo algorithm for hard spheres and related systems (see article with E. P. Bernard and D. B. Wilson, including Python implementation). This (fantastic) algorithm, about two orders of magnitude faster than local Monte Carlo, was used in our discovery of the first-order liquid-hexatic phase transition in hard disks. The method can be generalized to continuous potentials, and we used it to map out the phase diagrams of soft-disk systems. Look here for an implementation of the event-chain algorithm


Exact diagonalization algorithm for Dynamical mean field theory (see article with M. Caffarel). This algorithm has been instrumental in our discovery of a first-order Mott transition in the Hubbard model in infinite dimensions. Much of our early work in the field is written up in our review with Georges, Kotliar, and Rozenberg
Exact diagonalization algorithm for Dynamical mean field theory (see article with M. Caffarel). This algorithm has been instrumental in our discovery of a first-order Mott transition in the Hubbard model in infinite dimensions. Much of our early work in the field is written up in our review with Georges, Kotliar, and Rozenberg
Rejection-free cluster algorithm for dimers (see article with R. Moessner). This algorithm was used for our discovery of a critical phase in three-dimensional dimer models (paper with Huse, Sondhi, and Moessner). Note that dimers flip about a symmetry axis between one valid configuration and another.
Rejection-free cluster algorithm for dimers (see article with R. Moessner). This algorithm was used for our discovery of a critical phase in three-dimensional dimer models (paper with Huse, Sondhi, and Moessner). Note that dimers flip about a symmetry axis between one valid configuration and another.
Alder and Wainwright's event-driven Molecular Dynamics algorithm (1957). (Animation by Maxim Berman).
Alder and Wainwright's event-driven Molecular Dynamics algorithm (1957). (Animation by Maxim Berman).


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