Written in accessible language without mathematical formulas, this short book provides an overview of the wide and varied applications of artificial intelligence (AI) across the spectrum of physical sciences. Focusing in particular on AI's ability to extract patterns from data, known as machine learning (ML), the book includes a chapter on important machine learning algorithms and their respective applications in physics. It then explores the use of ML across a number of important sub-fields in more detail, ranging from particle, molecular and condensed matter physics, to astrophysics, cosmology and the theory of everything. The book covers such applications as the search for new particles and the detection of gravitational waves from the merging of black holes, and concludes by discussing what the future may hold.
Part I: Opening
1. Gathering the Team
Volker Knecht
2. Teamplay
Volker Knecht
3. The Rules of the Game
Volker Knecht and Kilian Hikaru Scheutwinkel
Part II: Machine-Learning the World from Subatomic to Cosmic Scales
4. AI for Particle Physics
Mario Campanelli and Volker Knecht
5. AI for Molecular Physics
Mayank Agrawal and Volker Knecht
6. AI for Condensed Matter Physics
Álvaro Díaz Fernández, Chao Fang, and Volker Knecht
7. AI for Cosmology
Kilian Hikaru Scheutwinkel, Daniel Grün, Bernard Jones, Jimena González Lozano, and Volker Knecht
Part III: Showdown
8. AI for Theory of Everything
Yang-Hui He and Volker Knecht
9. Conclusion and Outlook
Volker Knecht
Biography
Volker Knecht, Germany, Editor at International Journal of Molecular Sciences, Science Writer as Freelancer. Diploma in Physics at University of Kaiserslautern, PhD in Theoretical Physics at University of Göttingen, PhD project at MPI Göttingen, postdoc at University of Groningen, group leader and PI at MPI Potsdam and University of Freiburg. Research at the interface between physics, chemistry, biology, and computer science for 17 years.
"AI for Physics is a very recommendable, easy-to-read and wide-ranging review of applications that artificial intelligence can have in many of the branches of physics. Across its pages, Dr. Knecht, assisted by nine experts in the different fields, goes over the wide spectrum of scales in which machine learning can contribute to the research in physics, from the subatomic world to the whole universe as in cosmology. The book is written in very accessible language, which makes it a very good text for beginners that want to start learning about these subjects, providing them a general view of the current state of this very promising cooperation between AI and physics. But it is also a very interesting reading for those who, having certain knowledge in some of the applications of AI in physics, want to complete their view of the whole picture, and benefit from the big amount of good references spread along its lines, that make it easy to follow the reading with more specific texts."
-Dr. Óscar de Abril, Associate Professor, Technical University of Madrid, Spain
"Artificial intelligence is on a rapid path of revolutionizing science, upsetting long-established routines, and learning complex relationships in nature beyond what human minds can comprehend. AI for Physics hits the nail on the head in explaining how that is possible and where the future may take us. A great read for scientists and anybody else curious about how soon computers will be smarter than us."
-Michael Feig, Professor, Michigan State University
"In an "afternoon read", this new book, aimed at non-specialists, intends to update its readers on the emerging applications of machine learning in physics. Without equations and derivations, the book provides an intuitive understanding of what seems to be going on, while simultaneously reviewing many of the exciting applications of ML in physics, ranging from the astronomically large to the femto-meter sized small. A wealth of references allows to all these topics further self- study. I can readily recommend the book to all scientists interested in how ML is already shaping the way of science. Graduate students will find it especially interesting to quickly get an overview if ML has already entered their chosen research topic."
- Rudolf A. Roemer, Professor, University of Warwick
"Very well written, I have enjoyed reading it! "
- Parimal Kar, Associate Professor, Indian Institute of Technology Indore.
