Muutke küpsiste eelistusi

E-raamat: AI for Physics [Taylor & Francis e-raamat]

  • Formaat: 129 pages, 7 Line drawings, black and white; 3 Halftones, black and white; 10 Illustrations, black and white
  • Sari: AI for Everything
  • Ilmumisaeg: 29-Aug-2022
  • Kirjastus: CRC Press
  • ISBN-13: 9781003245186
Teised raamatud teemal:
  • Taylor & Francis e-raamat
  • Hind: 193,88 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
  • Tavahind: 276,97 €
  • Säästad 30%
  • Formaat: 129 pages, 7 Line drawings, black and white; 3 Halftones, black and white; 10 Illustrations, black and white
  • Sari: AI for Everything
  • Ilmumisaeg: 29-Aug-2022
  • Kirjastus: CRC Press
  • ISBN-13: 9781003245186
Teised raamatud teemal:
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.

Written in accessible language without mathematical formulas, the book covers important machine learning algorithms and their respective applications in physics and 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.
Acknowledgments xi
Contributors xiii
List of Abbreviations
xvii
Part I Opening
1 Gathering the Team
3(8)
Volker Knecht
AI and Machine Learning
3(2)
A Brief History of Physics
5(3)
References
8(3)
2 Teamplay
11(16)
Volker Knecht
Machine Learning Physics
11(3)
Impact of Physics on Machine Learning
14(1)
Statistical Physics of ML
15(1)
Analog Computers
16(1)
Quantum Computers
17(2)
Machine Learning the Physical World from Subatomic to Cosmic Scales
19(4)
References
23(4)
3 The Rules of the Game
27(18)
Volker Knecht
Kilian Hikaru Scheutwinkel
Supervised Learning
28(1)
Classification versus Regression
28(1)
Simple Mappings
29(1)
Complex Mappings
29(2)
River Deep -- Mountain High
31(1)
Choosing the Number of Parameters as a Balancing Act
32(1)
Bias-Variance Trade-off
33(1)
Kernel Methods
33(1)
Decision Trees
34(1)
Artificial Neural Networks
34(2)
Treating Uncertainty and Prior Knowledge: Bayesian Inference
36(1)
Symbolic Regression
37(1)
Unsupervised Learning
37(1)
Clustering and Principal Component Analysis
38(1)
Autoencoders
38(1)
Physics-Inspired Algorithm: Restricted Boltzmann Machine
39(1)
Generative Adversarial Networks
39(1)
Reinforcement Learning
40(1)
What's Next?
40(1)
References
40(5)
Part II Machine-Learning the World from Subatomic to Cosmic Scales
4 AI for Particle Physics
45(14)
Mario Campaneixi
Volker Knecht
The Standard Model
46(2)
Open Problems
48(1)
Theories beyond SM
49(1)
Machine Learning Particle Physics
49(1)
Cut-Based Event Selection in a Particle Physics Experiment
50(1)
Particle and Event Selection with Neural Networks and Boosted Decision Trees
50(1)
Machine Learning for Jet Physics
51(3)
Convolutional Neural Networks for Neutrino Experiments
54(2)
References
56(3)
5 AI for Molecular Physics
59(12)
Mayank Agrawal
Volker Knecht
Speeding Up Simulations I: Machine Learning Atomistic Force Fields
61(2)
Using Machine Learning to Analyze Output of Simulations
63(3)
Speeding Up Simulations II: Machine Learning Coarse-Grained Force Fields
66(2)
References
68(3)
6 AI for Condensed Matter Physics
71(12)
Alvaro Diaz Hernandez
Chao Fang
Volker Knecht
Using Machine Learning to Overcome Sampling Problem for Spin Glasses
72(3)
Machine Learning Topological Order Transition
75(2)
Machine Learning Quantum Many-Body Systems
77(1)
Looking from Outside: Machine Learning Quantum Tomography
78(1)
Machine Learning Based Design of New Materials and Quantum States
78(2)
References
80(3)
7 AI for Cosmology
83(20)
Kilian Hikaru Scheutwinkel
Daniel Grun
Bernard Jones
Jimena Gonzalez Lozano
Volker Knecht
The Concordance Model of Cosmology
83(2)
Machine Learning Big Data and the Global Shape of the Universe
85(2)
Machine Learning New Physics versus Instrumental Effects
87(1)
Machine Learning Photometric Redshift
88(1)
Objects in the Mirror May Be Bluer than They Appear
88(2)
AI to the Rescue - But with the Right Architecture and Training
90(1)
Machine Learning Cosmic Structure
91(1)
Bubble Universes All the Way Down
91(1)
Distortion Probes Gravitation: Interstellar Lensing
92(1)
Fishing for Complements with the Cosmic Web
93(1)
Machine Learning Gravitational Waves
94(4)
Note
98(1)
References
98(5)
Part III Showdown
8 AI for Theory of Everything
103(12)
Yang-Hui He
Volker Knecht
Physics and Geometry
103(1)
String Theory
104(1)
Extra Dimensions
104(1)
Why String Theory?
105(1)
Machine-Learning the Landscape
106(1)
The String Landscape and Vacuum Degeneracy Problem
107(2)
More on Machine-Learning the Landscape
109(4)
Epilogue
113(1)
References
113(2)
9 Conclusion and Outlook
115(2)
Volker Knecht
References 117(2)
Appendix: Table of Contents for Electronic Supplement 119(2)
Index 121
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.