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      AI for physics

      한글로보기

      https://www.riss.kr/link?id=M16596511

      • 저자
      • 발행사항

        Boca Raton : CRC Press, 2023

      • 발행연도

        2023

      • 작성언어

        영어

      • 주제어
      • DDC

        006.3 판사항(23)

      • ISBN

        9781032156552
        9781032151694

      • 자료형태

        단행본(다권본)

      • 발행국(도시)

        Florida

      • 서명/저자사항

        AI for physics / Volker Knecht, with contributions from Kilian Hikaru Scheutwinkel, Mario Campanelli, Mayank Agrawal, Álvara Díaz Fernández, Chao Fang, Daniel Grün, Bernard Jones, Jimena González Lozano, Yang-Hui He.

      • 판사항

        1st ed

      • 형태사항

        xviii, 129 p. : ill. ; 21 cm.

      • 총서사항

        AI for everything series ; [v. 2] AI for everything series; [v. 2].

      • 일반주기명

        Includes bibliographical references and index.

      • 소장기관
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        • 국립중앙도서관 국립중앙도서관 우편복사 서비스
        • 동국대학교 중앙도서관 소장기관정보
        • 숭실대학교 도서관 소장기관정보
        • 한국과학기술원(KAIST) 학술문화관 소장기관정보
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      부가정보

      목차 (Table of Contents)

      • CONTENTS
      • Acknowledgments = xi
      • Contributors = xiii
      • List of Abbreviations = xvii
      • Part I. Opening
      • CONTENTS
      • Acknowledgments = xi
      • Contributors = xiii
      • List of Abbreviations = xvii
      • Part I. Opening
      • 1 Gathering the Team = 3
      • AI and Machine Learning = 3
      • A Brief History of Physics = 5
      • References = 8
      • 2 Teamplay = 11
      • Machine Learning Physics = 11
      • Impact of Physics on Machine Learning = 14
      • Statistical Physics of ML = 15
      • Analog Computers = 16
      • Quantum Computers = 17
      • Machine Learning the Physical World from Subatomic to Cosmic Scales = 19
      • References = 23
      • 3 The Rules of the Game = 27
      • Supervised Learning = 28
      • Classification versus Regression = 28
      • Simple Mappings = 29
      • Complex Mappings = 29
      • River Deep - Mountain High = 31
      • Choosing the Number of Parameters as a Balancing Act = 32
      • Bias-Variance Trade-off = 33
      • Kernel Methods = 33
      • Decision Trees = 34
      • Artificial Neural Networks = 34
      • Treating Uncertainty and Prior Knowledge : Bayesian Inference = 36
      • Symbolic Regression = 37
      • Unsupervised Learning = 37
      • Clustering and Principal Component Analysis = 38
      • Autoencoders = 38
      • Physics-Inspired Algorithm : Restricted Boltzmann Machine = 39
      • Generative Adversarial Networks = 39
      • Reinforcement Learning = 40
      • What’s Next? = 40
      • References = 40
      • Part II. Machine-Learning the World from Subatomic to Cosmic Scales
      • 4 AI for Particle Physics = 45
      • The Standard Model = 46
      • Open Problems = 48
      • Theories beyond SM = 49
      • Machine Learning Particle Physics = 49
      • Cut-Based Event Selection in a Particle Physics Experiment = 50
      • Particle and Event Selection with Neural Networks and Boosted Decision Trees = 50
      • Machine Learning for Jet Physics = 51
      • Convolutional Neural Networks for Neutrino Experiments = 54
      • References = 56
      • 5 AI for Molecular Physics = 59
      • Speeding Up Simulations I : Machine Learning Atomistic Force Fields = 61
      • Using Machine Learning to Analyze Output of Simulations = 63
      • Speeding up Simulations II : Machine Learning Coarse-Grained Force Fields = 66
      • References = 68
      • 6 AI for Condensed Matter Physics = 71
      • Using Machine Learning to Overcome Sampling Problem for Spin Glasses = 72
      • Machine Learning Topological Order Transition = 75
      • Machine Learning Quantum Many-Body Systems = 77
      • Looking from Outside : Machine Learning Quantum Tomography = 78
      • Machine Learning Based Design of New Materials and Quantum States = 78
      • References = 80
      • 7 AI for Cosmology = 83
      • The Concordance Model of Cosmology = 83
      • Machine Learning Big Data and the Global Shape of the Universe = 85
      • Machine Learning New Physics versus Instrumental Effects = 87
      • Machine Learning Photometric Redshift = 88
      • Objects in the Mirror May Be Bluer Than They Appear = 88
      • AI to the Rescue - But with the Right Architecture and Training = 90
      • Machine Learning Cosmic Structure = 91
      • Bubble Universes All the Way Down = 91
      • Distortion Probes Gravitation : Interstellar Lensing = 92
      • Fishing for Complements with the Cosmic Web = 93
      • Machine Learning Gravitational Waves = 94
      • Note = 98
      • References = 98
      • Part III. Showdown
      • 8 AI for Theory of Everything = 103
      • Physics and Geometry = 103
      • String Theory = 104
      • Extra Dimensions = 104
      • Why String Theory? = 105
      • Machine-Learning the Landscape = 106
      • The String Landscape and Vacuum Degeneracy Problem = 107
      • More on Machine-Learning the Landscape = 109
      • Epilogue = 113
      • References = 113
      • 9 Conclusion and Outlook = 115
      • References = 117
      • Appendix : Table of Contents for Electronic Supplement = 119
      • Index = 121
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