Markov Chain Monte Carlo Methods in Quantum Field Theories: A Modern Primer

Author:   Anosh Joseph
Publisher:   Springer Nature Switzerland AG
Edition:   1st ed. 2020
ISBN:  

9783030460433


Pages:   126
Publication Date:   17 April 2020
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Markov Chain Monte Carlo Methods in Quantum Field Theories: A Modern Primer


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Overview

This primer is a comprehensive collection of analytical and numerical techniques that can be used to extract the non-perturbative physics of quantum field theories. The intriguing connection between Euclidean Quantum Field Theories (QFTs) and statistical mechanics can be used to apply Markov Chain Monte Carlo (MCMC) methods to investigate strongly coupled QFTs. The overwhelming amount of reliable results coming from the field of lattice quantum chromodynamics stands out as an excellent example of MCMC methods in QFTs in action. MCMC methods have revealed the non-perturbative phase structures, symmetry breaking, and bound states of particles in QFTs. The applications also resulted in new outcomes due to cross-fertilization with research areas such as AdS/CFT correspondence in string theory and condensed matter physics. The book is aimed at advanced undergraduate students and graduate students in physics and applied mathematics, and researchers in MCMC simulations and QFTs. At the end of this book the reader will be able to apply the techniques learned to produce more independent and novel research in the field.

Full Product Details

Author:   Anosh Joseph
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   1st ed. 2020
Weight:   0.454kg
ISBN:  

9783030460433


ISBN 10:   3030460436
Pages:   126
Publication Date:   17 April 2020
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Monte Carlo Method for Integration.- Monte Carlo with Importance Sampling.- Markov Chains.- Markov Chain Monte Carlo.- MCMC and Feynman Path Integrals.- Reliability of Simulations.- Hybrid (Hamiltonian) Monte Carlo.- MCMC and Quantum Field Theories on a Lattice.- Machine Learning and Quantum Field Theories.- C++ Programs.

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Author Information

Anosh Joseph is an Assistant Professor of Physics at the Indian Institute of Science Education and Research (IISER) Mohali, India. A graduate of Indian Institute of Technology (IIT) Madras, India, he obtained his PhD at Syracuse University, USA, in 2011. Since then, he has held post-doctoral Research Associate positions at the Los Alamos National Laboratory (LANL), USA; Deutsches Elektronen-Synchrotron (DESY), Germany; Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge, UK; and the International Centre for Theoretical Sciences (ICTS) of the Tata Institute of Fundamental Research (TIFR), India. As theoretical and computational physicist his research explores ideas to solve problems in strongly coupled quantum field theories, including Quantum Chromodynamics (QCD), supersymmetric field theories, and quantum field theories with complex actions. He has explored various non-perturbative phenomena occurring in field theories, such as phase transitions, bound states of elementary particles, and symmetry breaking using analytical and numerical methods. He has published numerous peer-reviewed journal articles on lattice quantum field theory, supersymmetric field theory, complex Langevin dynamics, and non-commutative field theory.

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