DEEP LEARNING FOR PHYSICS RESEARCH

By: Martin Erdmann, Jonas Glombitza, Gregor Kasieczka, Uwe Klemradt

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Hardcover | 5 July 2021

At a Glance

Hardcover
340 Pages

Dimensions(cm)
22.86 x 15.24 x 2.06

Hardcover

$256.75

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A core principle of physics is knowledge gained from data. Thus, deep learning has instantly entered physics and may become a new paradigm in basic and applied research. This textbook addresses physics students and physicists who want to understand what deep learning actually means and which potential it offers for their own scientific projects. Being familiar with linear algebra and parameter optimization is sufficient to jump-start deep learning. The authors of this book take a pragmatic approach, describe basic and advanced applications in physics research, and offer simple hands-on exercises for programming deep networks for which source code and training data can be downloaded.This book provides a comprehensive introduction to topological insulators, topological superconductors and topological semimetals. It includes all the mathematical background required for the subject. There are very few books with such a coverage in the market.

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