Titlebook: Quantum Field Theory I; Foundations and Abel Edouard B. Manoukian Textbook 2016 Springer International Publishing Switzerland 2016 Quantum

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Preliminaries,e us with a coherent description of nature in spite of the “patchwork” of seemingly different approaches that have appeared during the last 40 years or so, but still all, with the common goal of unification.

initiate

,Quantum Field Theory Methods of Spin 1∕2,e us with a coherent description of nature in spite of the “patchwork” of seemingly different approaches that have appeared during the last 40 years or so, but still all, with the common goal of unification.

Foam-Cells

Fundamental Aspects of Quantum Field Theory,e us with a coherent description of nature in spite of the “patchwork” of seemingly different approaches that have appeared during the last 40 years or so, but still all, with the common goal of unification.

MANIA

Abelian Gauge Theories,e us with a coherent description of nature in spite of the “patchwork” of seemingly different approaches that have appeared during the last 40 years or so, but still all, with the common goal of unification.

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Textbook 2016retical descriptions have been provided. The book is unique in that it offers a new approach to the subject and explores many topics merely touched upon, if covered at all, in standard reference works..A detailed and largely non-technical introductory chapter traces the development of QFT from its i

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Textbook 2016rmation also to be found in the abelian case, equal importance is put on both abelian and non-abelian gauge theories..Particular emphasis is placed on the concept of a quantum field and its particle content to provide an appropriate description of physical processes at high energies, where relativit

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ata along with the trap’s associated spatial coordinates, we build spatial Bayesian networks to explore species behaviours (how they move and distribute themselves) and interactions (how they distribute in relation to other species). This combination of probabilistic reasoning and deep learning offe