Artificial intelligence for scientific discoveries : extracting physical concepts from experimental data using deep learning / Raban Iten.
2023
Q180.55.D57 I84 2023
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Title
Artificial intelligence for scientific discoveries : extracting physical concepts from experimental data using deep learning / Raban Iten.
Author
ISBN
3031270193 electronic book
9783031270192 (electronic bk.)
9783031270185
3031270185
9783031270192 (electronic bk.)
9783031270185
3031270185
Published
Cham : Springer, 2023.
Language
English
Description
1 online resource (176 pages) : illustrations (black and white).
Item Number
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.
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Includes bibliographical references.
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Table of Contents
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.
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.