Deep Learning For Physics Research
Erdmann M, Glombitza J, Kasieczka G, Klemradt U (2021)
Publication Type: Authored book, Textbook
Publication year: 2021
Publisher: World Scientific Publishing Co.
ISBN: 9789811237461
URI: http://www.deeplearningphysics.org
DOI: 10.1142/12294
Abstract
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 what is the potential for their own scientific projects. Being familiar with linear algebra and parameter optimization is sufficient to jump-start deep learning. Adopting a pragmatic approach, basic and advanced applications in physics research are described. Also offered are simple hands-on exercises for implementing deep networks for which python code and training data can be downloaded.
Authors with CRIS profile
Involved external institutions
Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen
Germany (DE)
Universität Hamburg (UHH)
Germany (DE)
Germany (DE)
Universität Hamburg (UHH)
Germany (DE)
How to cite
APA:
Erdmann, M., Glombitza, J., Kasieczka, G., & Klemradt, U. (2021). Deep Learning For Physics Research. World Scientific Publishing Co..
MLA:
Erdmann, Martin, et al. Deep Learning For Physics Research. World Scientific Publishing Co., 2021.
BibTeX: Download