Title Artificial Intelligence for Scientific Discoveries : Extracting Physical Concepts from Experimental Data Using Deep Learning Author(s) Iten, Raban Publication Cham, 1. Imprint: Springer 2. Springer International Publishing, 2023. Description XIII, 170 p. 38 illus., 37 illus. in color : online resource Abstract Note 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. ISBN,Price 9783031270192 Keyword(s) 1. ARTIFICIAL INTELLIGENCE 2. Data Analysis and Big Data 3. DATA SCIENCE 4. EBOOK - SPRINGER 5. MATHEMATICAL PHYSICS 6. Quantitative research 7. Theoretical, Mathematical and Computational Physics Item Type eBook Multi-Media Links media link description Circulation Data Accession#   Call# Status Issued To Return Due On Physical Location I12704     On Shelf