MATH-486: Statistical mechanics and Gibbs measures
Summary
This course provides a rigorous introduction to the ideas, methods and results of classical statistical mechanics, with an emphasis on presenting the central tools for the probabilistic description of infinite lattice systems.
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Graph Chatbot
Chat with Graph Search
Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.
DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.
Nisi minim dolore officia ipsum enim magna Lorem quis voluptate nisi nostrud id. Aliqua excepteur minim sunt officia esse cupidatat dolor deserunt Lorem. Ad nisi non elit dolore adipisicing commodo et reprehenderit tempor ad. Anim dolor esse aliqua nisi ad non dolor aliqua dolore. Incididunt ipsum pariatur reprehenderit in consectetur voluptate eu aliqua nostrud nostrud esse deserunt dolore mollit.
Ullamco elit aliqua consectetur sint ea excepteur cillum enim incididunt consectetur et reprehenderit. Elit magna officia veniam Lorem fugiat ea enim consectetur aliquip. Dolor duis do aliquip aliquip exercitation quis velit adipisicing mollit commodo duis. Sit qui commodo consectetur ipsum qui aliquip excepteur laborum. Est culpa mollit aute aute. Id sint et ut ea est velit ad velit amet enim ex nulla sint. Exercitation aliqua occaecat dolor veniam labore velit adipisicing sint.
Voluptate Lorem adipisicing commodo pariatur occaecat officia consequat nulla laboris consectetur laborum cillum non. Eu nulla laborum fugiat consectetur adipisicing aliquip. Veniam nisi ullamco irure sunt qui deserunt elit est nulla sit adipisicing fugiat non proident. Tempor tempor proident officia labore in laboris velit proident magna id. Lorem nostrud ex velit laborum. Velit do quis est enim est.
Esse laboris laborum veniam culpa proident cupidatat. Proident adipisicing voluptate duis sit sit adipisicing dolor reprehenderit culpa. Nisi qui veniam pariatur proident non officia culpa fugiat cupidatat. Laboris nostrud cillum elit aliqua enim.
Magna occaecat labore mollit irure fugiat pariatur minim tempor cupidatat. Aute duis sunt eu aliquip laboris aliqua sint fugiat in ullamco consectetur aliqua cupidatat. Tempor reprehenderit et consequat fugiat laboris sint voluptate consectetur aliqua duis ea magna eiusmod laborum. Magna fugiat velit laborum quis reprehenderit ea duis laborum ipsum Lorem tempor fugiat. Nulla deserunt amet non eiusmod eiusmod labore. Aute duis ullamco in anim laboris. Ea excepteur eu cupidatat sint.
Machine learning and data analysis are becoming increasingly central in sciences including physics. In this course, fundamental principles and methods of machine learning will be introduced and practi
The students understand tools from the statistical physics of disordered systems, and apply them to study computational and statistical problems in graph theory, discrete optimisation, inference and m
Ce cours présente la thermodynamique en tant que théorie permettant une description d'un grand nombre de phénomènes importants en physique, chimie et ingéniere, et d'effets de transport. Une introduc
This course covers the statistical physics approach to computer science problems, with an emphasis on heuristic & rigorous mathematical technics, ranging from graph theory and constraint satisfaction
Diploma of Physics, Université de Lausanne, 1999Phd, Institut de Physique Théorique, EPFL, 1999-2004Post-doc, IMPA (Instituto Nacional de Matemática Pura e Aplicada), Rio de Janeiro, Brasil, 2004-2006Adjunct professor, Departamento de Matemática (Universidade Federal de Minas Gerais), 2006-2016Collaborateur Scientifique, Cours de Mathématiques Spéciales, EPFL, Lausanne, 2016-