AI for Physics: Machine Learning the World from Nuclear to Cosmic Scales

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