Søgning på underkategorier- og emner:
Bemærk: Kan ikke leveres før jul.
Designed for both researchers in the field and graduate students of physics, this book charts the development and theoretical analysis of molecular... Læs mere
Bemærk: Kan ikke leveres før jul.
Introducing graduate students and researchers to mathematical physics, this book discusses two recent developments. Providing... Læs mere
Bemærk: Kan ikke leveres før jul.
Written for advanced graduate students and researchers in elementary particle physics, cosmology, and related... Læs mere
Bemærk: Kan ikke leveres før jul.
Ernest William Brown (1866–1938) was a prominent British mathematician and astronomer renowned for his contribution to the... Læs mere
Bemærk: Kan ikke leveres før jul.
A modern introduction to statistics, this book is ideal for undergraduates in physics. It covers the basic topics as well as advanced and modern... Læs mere
Bemærk: Kan ikke leveres før jul.
An introductory textbook for standard undergraduate courses in thermodynamics, covering important quantum behaviours, classical thermodynamics and statistical mechanics.
Bemærk: Kan ikke leveres før jul.
Bemærk: Kan ikke leveres før jul.
This book is intended as a supplementary text to the standard course books on theoretical physics and astrophysics, addressing... Læs mere
Bemærk: Kan ikke leveres før jul.
This volume shows that the emergence of computational social science (CSS) is an endogenous response to problems from within the social sciences and not exogeneous.
Bemærk: Kan ikke leveres før jul.
This book presents a selection of the talks resulting from research carried out by different groups at the Centre... Læs mere
Bemærk: Kan ikke leveres før jul.
This book presents a comprehensive review of various aspects of the novel and rapidly developing field of active matter, which encompasses a wide variety of self-organized self-driven energy-consuming media or agents.
Bemærk: Kan ikke leveres før jul.
The book represents the first attempt to systematically deal with the use of deep neural networks to forecast chaotic time series.