AI for Physics

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

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.