Librería: California Books, Miami, FL, Estados Unidos de America
EUR 106,46
Cantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: New.
Idioma: Inglés
Publicado por Springer Nature Switzerland Ag, 2026
ISBN 10: 3032176344 ISBN 13: 9783032176349
Librería: Revaluation Books, Exeter, Reino Unido
EUR 141,99
Cantidad disponible: 2 disponibles
Añadir al carritoHardcover. Condición: Brand New. second edition 2026 edition. In Stock.
EUR 107,80
Cantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: New.
Idioma: Inglés
Publicado por Springer Nature Switzerland AG Aug 2026, 2026
ISBN 10: 3032176344 ISBN 13: 9783032176349
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 104,60
Cantidad disponible: 2 disponibles
Añadir al carritoBuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This second edition of the textbook Introduction to Tensor Network Methods contains more advanced and technical parts as new topics related to tensor network algorithms that have been developed in the last few years. The reader finds new chapters dedicated to tree tensor networks for high-dimensional systems as applications to lattice gauge theory. The implementation of tensor networks for machine learning is also presented in detail.This textbook gives an in-depth overview on the numerical simulation technique of tensor networks (TNs) with hands-on technical descriptions, work exercises and computation results. TNs have originally been developed for solving the quantum many-body problem and simulating quantum systems on a classical computer. However, as a mathematical tool, TNs have emerged as powerful theoretical and numerical versatile tools to attack more generally hard mathematical problems. In particular, their range application has expanded to combinatorial optimization and even as an alternative tool for machine learning in the field of artificial intelligence. This textbook introduces the reader to the field, describing the main principles and core mathematical concepts in the light of its application in quantum physics and, along the way, touches on the application of TNs to problems from various fields, ranging from low-energy to high-energy physics up to medical physics and machine learning.