Artificial Intelligence for Scientific Discoveries

Extracting Physical Concepts from Experimental Data Using Deep Learning

Raban Iten (Autor)

Buch | Hardcover

XIII, 170 Seiten

2023 | 2023
Springer International Publishing (Verlag)
978-3-031-27018-5 (ISBN)

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This book explains the modern approach to discovering physical concepts with machine learning and elucidates its strengths and limitations. SciNet finds the relevant physical parameters, such as the mass of a particle, from experimental data and makes predictions based on the parameters found.

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.

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. Furthermore, he developed algorithms for quantum compilers and contributed to various open-source libraries for quantum computing.

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.

Erscheinungsdatum	13.04.2023
Zusatzinfo	XIII, 170 p. 38 illus., 37 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Gewicht	439 g
Themenwelt	Mathematik / Informatik ► Informatik ► Datenbanken
	Informatik ► Theorie / Studium ► Algorithmen
	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
	Naturwissenschaften ► Physik / Astronomie ► Allgemeines / Lexika
	Naturwissenschaften ► Physik / Astronomie ► Theoretische Physik
Schlagworte	AI-Scientist • Artificial Intelligence • Automation of Physics • Deep learning • Discovering Physical Laws • Extracting Equations from Data • Heliocentric Solar System • machine learning • Neural networks • representation learning
ISBN-10	3-031-27018-5 / 3031270185
ISBN-13	978-3-031-27018-5 / 9783031270185
Zustand	Neuware