Deep Learning For Physics Research

Deep learning is a physics principle that can be used for basic and applied research. It requires familiarity with linear algebra and parameter optimization and offers hands-on exercises with python code and training data.

Format: Hardback
Length: 340 pages
Publication date: 28 June 2021
Publisher: World Scientific Publishing Co Pte Ltd

Physics is a discipline that relies heavily on data-driven knowledge acquisition, and deep learning has made a significant impact on this field. Deep learning has rapidly permeated physics and has the potential to revolutionize both basic and applied research. This textbook is designed to cater to physics students and physicists who are interested in understanding the intricacies of deep learning and exploring its potential for their scientific endeavors.

To delve into deep learning, a foundational understanding of linear algebra and parameter optimization is necessary. This knowledge serves as a solid foundation for navigating the complexities of deep learning models. The textbook adopts a pragmatic approach, presenting both basic and advanced applications of deep learning in physics research. It provides detailed explanations and examples to facilitate a clear understanding of the subject matter. Additionally, the book offers simple hands-on exercises that allow readers to implement deep networks using Python code and access the corresponding training data.

By leveraging the power of deep learning, physicists can explore new frontiers in their field and uncover previously unknown phenomena. The potential applications of deep learning in physics are vast, ranging from imaging and simulation to materials science and astrophysics. With this textbook as a guide, readers will gain the skills and knowledge necessary to harness the power of deep learning and make meaningful contributions to the field of physics.

Weight: 650g
Dimension: 158 x 234 x 22 (mm)
ISBN-13: 9789811237454