Artificial intelligence for scientific discoveries : extracting physical concepts from experimental data using deep learning / Raban Iten.
Iten, Raban, author.
2023
Q180.55.D57 I84 2023
Available Online

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Title Artificial intelligence for scientific discoveries : extracting physical concepts from experimental data using deep learning / Raban Iten.
Author Iten, Raban, author.
ISBN 3031270193 electronic book
9783031270192 (electronic bk.)
9783031270185
3031270185
DOI https://doi.org/10.1007/978-3-031-27019-2
Published Cham : Springer, 2023.
Language English
Description 1 online resource (176 pages) : illustrations (black and white).
Other Standard Identifiers 10.1007/978-3-031-27019-2 doi
Call Number Q180.55.D57 I84 2023
Dewey Decimal Classification 507.2
Summary 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.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Description based on print version record.
Available in Other Form Artificial intelligence for scientific discoveries.
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Record Appears in Online Resources > Ebooks
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Introduction
Machine Learning Background
Overview of Using Machine Learning for Physical Discoveries
Theory: Formalizing the Process of Human Model Building
Methods: Using Neural Networks to Find Simple Representations
Applications: Physical Toy Examples
Open Questions and Future Prospects.

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