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.
Author(s): Volker Knecht
Series: AI for Everything
Publisher: CRC Press
Year: 2022
Language: English
Pages: 129
City: Boca Raton
Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Acknowledgments
Contributors
List of Abbreviations
Part I Opening
Chapter 1 Gathering the Team
AI and Machine Learning
A Brief History of Physics
References
Chapter 2 Teamplay
Machine Learning Physics
Impact of Physics on Machine Learning
Statistical Physics of ML
Analog Computers
Quantum Computers
Machine Learning the Physical World from Subatomic to Cosmic Scales
References
Chapter 3 The Rules of the Game
Supervised Learning
Classification versus Regression
Simple Mappings
Complex Mappings
River Deep – Mountain High
Choosing the Number of Parameters as a Balancing Act
Bias-Variance Trade-off
Kernel Methods
Decision Trees
Artificial Neural Networks
Treating Uncertainty and Prior Knowledge: Bayesian Inference
Symbolic Regression
Unsupervised Learning
Clustering and Principal Component Analysis
Autoencoders
Physics-Inspired Algorithm: Restricted Boltzmann Machine
Generative Adversarial Networks
Reinforcement Learning
What’s Next?
References
Part II Machine-Learning the World from Subatomic to Cosmic Scales
Chapter 4 AI for Particle Physics
The Standard Model
Open Problems
Theories beyond SM
Machine Learning Particle Physics
Cut-Based Event Selection in a Particle Physics Experiment
Particle and Event Selection with Neural Networks and Boosted Decision Trees
Machine Learning for Jet Physics
Convolutional Neural Networks for Neutrino Experiments
References
Chapter 5 AI for Molecular Physics
Speeding Up Simulations I: Machine Learning Atomistic Force Fields
Using Machine Learning to Analyze Output of Simulations
Speeding up Simulations II: Machine Learning Coarse-Grained Force Fields
References
Chapter 6 AI for Condensed Matter Physics
Using Machine Learning to Overcome Sampling Problem for Spin Glasses
Machine Learning Topological Order Transition
Machine Learning Quantum Many-Body Systems
Looking from Outside: Machine Learning Quantum Tomography
Machine Learning Based Design of New Materials and Quantum States
References
Chapter 7 AI for Cosmology
The Concordance Model of Cosmology
Machine Learning Big Data and the Global Shape of the Universe
Machine Learning New Physics versus Instrumental Effects
Machine Learning Photometric Redshift
Objects in the Mirror May Be Bluer Than They Appear
AI to the Rescue – But with the Right Architecture and Training
Machine Learning Cosmic Structure
Bubble Universes All the Way Down
Distortion Probes Gravitation: Interstellar Lensing
Fishing for Complements with the Cosmic Web
Machine Learning Gravitational Waves
Note
References
Part III Showdown
Chapter 8 AI for Theory of Everything
Physics and Geometry
String Theory
Extra Dimensions
Why String Theory?
Machine-Learning the Landscape
The String Landscape and Vacuum Degeneracy Problem
More on Machine-Learning the Landscape
Epilogue
References
Chapter 9 Conclusion and Outlook
References
Appendix: Table of Contents for Electronic Supplement
Index
