LEADER 03476nam 22006615 450 001 9910698653503321 005 20251202165942.0 010 $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(MiAaPQ)EBC7236375 035 $a(EXLCZ)995590000001037559 100 $a20230411d2023 u| 0 101 0 $aeng 135 $aurnn#---mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aArtificial Intelligence for Scientific Discoveries $eExtracting Physical Concepts from Experimental Data Using Deep Learning /$fby Raban Iten 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 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 $aMathematical physics 606 $aArtificial intelligence 606 $aArtificial intelligence$xData processing 606 $aQuantitative research 606 $aTheoretical, Mathematical and Computational Physics 606 $aArtificial Intelligence 606 $aData Science 606 $aData Analysis and Big Data 615 0$aMathematical physics. 615 0$aArtificial intelligence. 615 0$aArtificial intelligence$xData processing. 615 0$aQuantitative research. 615 14$aTheoretical, Mathematical and Computational Physics. 615 24$aArtificial Intelligence. 615 24$aData Science. 615 24$aData Analysis and Big Data. 676 $a006.3 676 $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