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Artificial Intelligence For Science: A Deep Learning Revolution [Hardback]

Edited by (University Of Virginia, Usa), Edited by (Rutherford Appleton Laboratory, Uk), Edited by (Northwestern University, Usa)
  • Formāts: Hardback, 804 pages
  • Izdošanas datums: 25-Apr-2023
  • Izdevniecība: World Scientific Publishing Co Pte Ltd
  • ISBN-10: 9811265666
  • ISBN-13: 9789811265662
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  • Hardback
  • Cena: 178,26 €
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  • Formāts: Hardback, 804 pages
  • Izdošanas datums: 25-Apr-2023
  • Izdevniecība: World Scientific Publishing Co Pte Ltd
  • ISBN-10: 9811265666
  • ISBN-13: 9789811265662
Citas grāmatas par šo tēmu:
This unique collection introduces AI, Machine Learning (ML), and deep neural network technologies leading to scientific discovery from the datasets generated both by supercomputer simulation and by modern experimental facilities.Huge quantities of experimental data come from many sources telescopes, satellites, gene sequencers, accelerators, and electron microscopes, including international facilities such as the Large Hadron Collider (LHC) at CERN in Geneva and the ITER Tokamak in France. These sources generate many petabytes moving to exabytes of data per year. Extracting scientific insights from these data is a major challenge for scientists, for whom the latest AI developments will be essential.The timely handbook benefits professionals, researchers, academics, and students in all fields of science and engineering as well as AI, ML, and neural networks. Further, the vision evident in this book inspires all those who influence or are influenced by scientific progress.
About the Editors v
Part A Introduction to AI for Science
1(26)
1 AI for Science
3(10)
Alok Choudhary
Geoffrey Fox
Tony Hey
2 The AI for Science Book in a Nutshell
13(14)
Alok Choudhary
Geoffrey Fox
Tony Hey
Part B Setting the Scene
27(152)
3 Data-Driven Science in the Era of AI: From Patterns to Practice
29(24)
Alexander Sandor Szalay
4 AI in the Broader Context of Data Science
53(14)
Rafael C. Alvarado
Philip E. Bourne
5 AlphaFold --- The End of the Protein Folding Problem or the Start of Something Bigger?
67(14)
David T. Jones
Janet M. Thornton
6 Applications of AI in Astronomy
81(14)
S. G. Djorgovski
A. A. Mahabal
M. J. Graham
K. Polsterer
A. Krone-Martins
7 Machine Learning for Complex Instrument Design and Optimization
95(22)
Barry C. Barish
Jonathan Richardson
Evangelos E. Papalexakis
Rutuja Gurav
8 Artificial Intelligence (AI) and Machine Learning (ML) at Experimental Facilities
117(28)
J. A. Sethian
J. J. Donatelli
A. Hexemer
M. M. Noack
D. M. Pelt
D. M. Ushizima
P. H. Zwart
9 The First Exascale Supercomputer Accelerating Al-for-Science and Beyond
145(18)
Satoshi Matsuoka
Kento Sato
Mohamed Wahib
Aleksandr Drozd
10 Benchmarking for AI for Science
163(16)
Jeyan Thiyagalingam
Mallikarjun Shankar
Geoffrey Fox
Tony Hey
Part C Exploring Application Domains
179(2)
Astronomy and Cosmology
181(38)
11 Radio Astronomy and the Square Kilometre Array
183(20)
Anna Scaife
12 AI for Astronomy: The Rise of the Machines
203(16)
Andrew Connolly
Climate Change
219(50)
13 AI for Net-Zero
221(22)
Alberto Arribas
Karin Strauss
Sharon Gillett
Amy Luers
Trevor Dhu
Lucas Joppa
Roy Zimmermann
Vanessa Miller
14 AI for Climate Science
243(26)
Philip Stier
Energy
269(34)
15 Accelerating Fusion Energy with AI
271(14)
R. Michael Churchill
Mark D. Boyer
Steven C. Cowley
16 Artificial Intelligence for a Resilient and Flexible Power Grid
285(18)
Olufemi A. Omitaomu
Jin Dong
Teja Kuruganti
Environmental Science
303(32)
17 AI and Machine Learning in Observing Earth from Space
305(14)
Jeff Dozier
18 Artificial Intelligence in Plant and Agricultural Research
319(16)
Sabina Leonelli
Hugh F. Williamson
Health
335(44)
19 AI and Pathology: Steering Treatment and Predicting Outcomes
337(18)
Rajarsi Gupta
Jakub Kaczmarzyk
Soma Kobayashi
Tahsin Kurc
Joel Saltz
20 The Role of Artificial Intelligence in Epidemiological Modeling
355(24)
Aniruddha Adiga
Srinivasan Venkatramanan
Jiangzhuo Chen
Przemyslaw Porebski
Amanda Wilson
Kenning Mortveit
Bryan Lewis
Justin Crow
Madhav V. Marathe
Nssac-Bii Team
Life Sciences
379(32)
21 Big AI: Blending Big Data with Big Theory to Build Virtual Humans
381(18)
Peter Coveney
Roger Highfield
22 A Roadmap for Defining Machine Learning Standards in Life Sciences
399(12)
Fotis Psomopoulos
Carole Goble
Leyla Jael Castro
Jennifer Harrow
Silvio C. E. Tosatto
Materials Science and Engineering
411(34)
23 Artificial Intelligence for Materials
413(18)
Debra J. Audus
Kamal Choudhary
Brian L. Decost
A. Gilad Kusne
Francesca Tavazza
James A. Warren
24 Artificial Intelligence for Accelerating Materials Discovery
431(14)
Ankit Agrawal
Alok Choudhary
Particle Physics
445(48)
25 Experimental Particle Physics and Artificial Intelligence
447(18)
David Rousseau
26 AI and Theoretical Particle Physics
465(28)
Rajan Gupta
Tanmoy Bhattacharya
Boram Yoon
Part D The Ecosystem of AI for Science
493(148)
27 Schema.org for Scientific Data
495(20)
Alasdair Gray
Leyla J. Castro
Nick Juty
Carole Goble
28 Al-coupled HPC Workflows
515(20)
Shantenu Jha
Vincent Pascuzzi
Matteo Turilli
29 AI for Scientific Visualization
535(18)
Chris R. Johnson
Han-Wei Shen
30 Uncertainty Quantification in AI for Science
553(18)
Tanmoy Bhattacharya
Cristina Garcia Cardona
Jamaludin Mohd-Yusof
31 AI for Next-Generation Global Network-Integrated Systems and Testbeds
571(38)
Mariam Kiran
Harvey B. Neumann
32 AI for Optimal Experimental Design and Decision-Making
609(18)
Francis J. Alexander
Kristofer-Roy Reyes
Lav R. Varshney
Byung-Jun Yoon
33 FAIR: Making Data AI-Ready
627(14)
Susanna-Assunta Sansone
Philippe Rocca-Serra
Mark Wilkinson
Lee Harland
Part E Perspectives on AI for Science
641(82)
34 Large Language Models for Science
643(28)
Austin Clyde
Arvind Ramanathan
Rick Stevens
35 AI for Autonomous Vehicles
671(8)
Tom St. John
Vijay Janapa Reddi
36 The Automated AI-driven Future of Scientific Discovery
679(14)
Hector Zenil
Ross D. King
37 Towards Reflection Competencies in Intelligent Systems for Science
693(14)
Yolanda Gil
38 The Interface of Machine Learning and Causal Inference
707(16)
Mohammad Taha Bahadori
David E. Heckerman
Part F Endpiece: AI Tools and Concepts
723(44)
39 Overview of Deep Learning and Machine Learning
725(18)
Alok Choudhary
Geoffrey Fox
Tony Hey
40 Topics, Concepts, and AI Methods Discussed in
Chapters
743(24)
Alok Choudhary
Geoffrey Fox
Tony Hey
Index 767