Overview
- Provides an overview for scientists of how machine learning can help to discover physical concepts
- Introduces a general framework that can help the reader to extract relevant parameters from experimental data
- The content of the book is easily accessible even to scientists without background knowledge in machine learning
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About this book
Will research soon be done by artificial intelligence, thereby making human researchers superfluous? This book explains modern approaches to discovering physical concepts with machine learning and elucidates their strengths and limitations. The automation of the creation of experimental setups and physical models, as well as model testing are discussed. The focus of the book is the automation of an important step of the model creation, namely finding a minimal number of natural parameters that contain sufficient information to make predictions about the considered system. The basic idea of this approach is to employ a deep learning architecture, SciNet, to model a simplified version of a physicist's reasoning process. SciNet finds the relevant physical parameters, like the mass of a particle, from experimental data and makes predictions based on the parameters found. The author demonstrates how to extract conceptual information from such parameters, e.g., Copernicus' conclusion that the solar system is heliocentric.
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Keywords
Table of contents (12 chapters)
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Front Matter
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Machine Learning Background
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Front Matter
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Overview of Using Machine Learning for Scientific Discoveries
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Front Matter
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Representation Learning for Physical Discoveries
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Front Matter
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Future Prospects
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Front Matter
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Back Matter
Authors and Affiliations
About the author
Raban Iten studied Physics and Mathematics at ETH Zürich, followed by a Ph.D. in quantum computation. During his Ph.D., he worked on using machine learning to discover physical concepts from experimental data of classical and quantum systems. This work was widely covered in the media and pointed out as a research highlight of 2019 by Nature Reviews Physics. Furthermore, he developed algorithms for quantum compilers and contributed to various open-source libraries for quantum computing.
Bibliographic Information
Book Title: Artificial Intelligence for Scientific Discoveries
Book Subtitle: Extracting Physical Concepts from Experimental Data Using Deep Learning
Authors: Raban Iten
DOI: https://doi.org/10.1007/978-3-031-27019-2
Publisher: Springer Cham
eBook Packages: Physics and Astronomy, Physics and Astronomy (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-031-27018-5Published: 12 April 2023
Softcover ISBN: 978-3-031-27021-5Published: 13 April 2024
eBook ISBN: 978-3-031-27019-2Published: 11 April 2023
Edition Number: 1
Number of Pages: XIII, 170
Number of Illustrations: 1 b/w illustrations, 37 illustrations in colour
Topics: Theoretical, Mathematical and Computational Physics, Artificial Intelligence, Data Structures and Information Theory, Statistics, general