Acknowledgments |
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xi | |
Contributors |
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xiii | |
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xvii | |
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3 | (8) |
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3 | (2) |
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A Brief History of Physics |
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5 | (3) |
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8 | (3) |
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11 | (16) |
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11 | (3) |
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Impact of Physics on Machine Learning |
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14 | (1) |
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Statistical Physics of ML |
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15 | (1) |
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16 | (1) |
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17 | (2) |
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Machine Learning the Physical World from Subatomic to Cosmic Scales |
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19 | (4) |
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23 | (4) |
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27 | (18) |
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Kilian Hikaru Scheutwinkel |
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28 | (1) |
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Classification versus Regression |
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28 | (1) |
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29 | (1) |
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29 | (2) |
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River Deep -- Mountain High |
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31 | (1) |
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Choosing the Number of Parameters as a Balancing Act |
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32 | (1) |
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33 | (1) |
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33 | (1) |
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34 | (1) |
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Artificial Neural Networks |
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34 | (2) |
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Treating Uncertainty and Prior Knowledge: Bayesian Inference |
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36 | (1) |
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37 | (1) |
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37 | (1) |
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Clustering and Principal Component Analysis |
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38 | (1) |
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38 | (1) |
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Physics-Inspired Algorithm: Restricted Boltzmann Machine |
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39 | (1) |
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Generative Adversarial Networks |
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39 | (1) |
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40 | (1) |
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40 | (1) |
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40 | (5) |
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Part II Machine-Learning the World from Subatomic to Cosmic Scales |
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4 AI for Particle Physics |
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45 | (14) |
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46 | (2) |
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48 | (1) |
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49 | (1) |
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Machine Learning Particle Physics |
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49 | (1) |
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Cut-Based Event Selection in a Particle Physics Experiment |
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50 | (1) |
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Particle and Event Selection with Neural Networks and Boosted Decision Trees |
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50 | (1) |
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Machine Learning for Jet Physics |
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51 | (3) |
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Convolutional Neural Networks for Neutrino Experiments |
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54 | (2) |
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56 | (3) |
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5 AI for Molecular Physics |
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59 | (12) |
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Speeding Up Simulations I: Machine Learning Atomistic Force Fields |
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61 | (2) |
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Using Machine Learning to Analyze Output of Simulations |
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63 | (3) |
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Speeding Up Simulations II: Machine Learning Coarse-Grained Force Fields |
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66 | (2) |
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68 | (3) |
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6 AI for Condensed Matter Physics |
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71 | (12) |
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Using Machine Learning to Overcome Sampling Problem for Spin Glasses |
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72 | (3) |
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Machine Learning Topological Order Transition |
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75 | (2) |
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Machine Learning Quantum Many-Body Systems |
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77 | (1) |
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Looking from Outside: Machine Learning Quantum Tomography |
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78 | (1) |
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Machine Learning Based Design of New Materials and Quantum States |
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78 | (2) |
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80 | (3) |
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83 | (20) |
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Kilian Hikaru Scheutwinkel |
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The Concordance Model of Cosmology |
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83 | (2) |
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Machine Learning Big Data and the Global Shape of the Universe |
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85 | (2) |
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Machine Learning New Physics versus Instrumental Effects |
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87 | (1) |
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Machine Learning Photometric Redshift |
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88 | (1) |
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Objects in the Mirror May Be Bluer than They Appear |
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88 | (2) |
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AI to the Rescue - But with the Right Architecture and Training |
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90 | (1) |
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Machine Learning Cosmic Structure |
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91 | (1) |
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Bubble Universes All the Way Down |
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91 | (1) |
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Distortion Probes Gravitation: Interstellar Lensing |
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92 | (1) |
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Fishing for Complements with the Cosmic Web |
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93 | (1) |
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Machine Learning Gravitational Waves |
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94 | (4) |
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98 | (1) |
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98 | (5) |
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8 AI for Theory of Everything |
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103 | (12) |
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103 | (1) |
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104 | (1) |
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104 | (1) |
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105 | (1) |
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Machine-Learning the Landscape |
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106 | (1) |
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The String Landscape and Vacuum Degeneracy Problem |
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107 | (2) |
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More on Machine-Learning the Landscape |
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109 | (4) |
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113 | (1) |
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113 | (2) |
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115 | (2) |
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References |
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117 | (2) |
Appendix: Table of Contents for Electronic Supplement |
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119 | (2) |
Index |
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121 | |