LEADER 02860nam 2200505 450 001 9910698653503321 005 20230731000328.0 010 $a3-031-27019-3 010 $a9783031270192$b(ebook) 024 7 $a10.1007/978-3-031-27019-2 035 $a(CKB)5590000001037559 035 $a(DE-He213)978-3-031-27019-2 035 $a(MiAaPQ)EBC7236713 035 $a(Au-PeEL)EBL7236713 035 $a(PPN)269660666 035 $a(EXLCZ)995590000001037559 100 $a20230731d2023 uy 0 101 0 $aeng 135 $aurnn#---mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aArtificial Intelligence for Scientific Discoveries $eExtracting Physical Concepts from Experimental Data Using Deep Learning /$fRaban Iten 205 $aFirst edition. 210 1$aCham, Switzerland :$cSpringer,$d[2023] 210 4$dİ2023 215 $a1 online resource $cillustrations 311 08$a3-031-27018-5 311 08$a9783031270185 320 $aIncludes bibliographical references. 327 $aIntroduction -- 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. 330 $aWill 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. . 606 $aArtificial intelligence 606 $aDiscoveries in science 615 0$aArtificial intelligence. 615 0$aDiscoveries in science. 676 $a006.3 700 $aIten$b Raban$01352779 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910698653503321 996 $aArtificial Intelligence for Scientific Discoveries$93200556 997 $aUNINA