001463394 000__ 03676cam\a22005897i\4500
001463394 001__ 1463394
001463394 003__ OCoLC
001463394 005__ 20230601003318.0
001463394 006__ m\\\\\o\\d\\\\\\\\
001463394 007__ cr\cn\nnnunnun
001463394 008__ 230421s2023\\\\sz\a\\\\ob\\\\000\0\eng\d
001463394 019__ $$a1376174888
001463394 020__ $$a3031270193$$qelectronic book
001463394 020__ $$a9783031270192$$q(electronic bk.)
001463394 020__ $$z9783031270185
001463394 020__ $$z3031270185
001463394 0247_ $$a10.1007/978-3-031-27019-2$$2doi
001463394 035__ $$aSP(OCoLC)1376834571
001463394 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dUKAHL$$dYDX$$dN$T
001463394 049__ $$aISEA
001463394 050_4 $$aQ180.55.D57$$bI84 2023
001463394 08204 $$a507.2$$223/eng/20230421
001463394 1001_ $$aIten, Raban,$$eauthor.
001463394 24510 $$aArtificial intelligence for scientific discoveries :$$bextracting physical concepts from experimental data using deep learning /$$cRaban Iten.
001463394 264_1 $$aCham :$$bSpringer,$$c2023.
001463394 300__ $$a1 online resource (176 pages) :$$billustrations (black and white).
001463394 336__ $$atext$$btxt$$2rdacontent
001463394 337__ $$acomputer$$bc$$2rdamedia
001463394 338__ $$aonline resource$$bcr$$2rdacarrier
001463394 504__ $$aIncludes bibliographical references.
001463394 5050_ $$aIntroduction -- Machine Learning Background -- Overview of Using Machine Learning for Physical Discoveries -- Theory: Formalizing the Process of Human Model Building -- Methods: Using Neural Networks to Find Simple Representations -- Applications: Physical Toy Examples -- Open Questions and Future Prospects.
001463394 506__ $$aAccess limited to authorized users.
001463394 520__ $$aWill research soon be done by artificial intelligence, thereby making human researchers superfluous? This book explains modern approaches to discovering physical concepts with machine learning and elucidates their strengths and limitations. The automation of the creation of experimental setups and physical models, as well as model testing are discussed. The focus of the book is the automation of an important step of the model creation, namely finding a minimal number of natural parameters that contain sufficient information to make predictions about the considered system. The basic idea of this approach is to employ a deep learning architecture, SciNet, to model a simplified version of a physicist's reasoning process. SciNet finds the relevant physical parameters, like the mass of a particle, from experimental data and makes predictions based on the parameters found. The author demonstrates how to extract conceptual information from such parameters, e.g., Copernicus' conclusion that the solar system is heliocentric.
001463394 588__ $$aDescription based on print version record.
001463394 650_0 $$aArtificial intelligence.
001463394 650_0 $$aDiscoveries in science$$xData processing.
001463394 655_0 $$aElectronic books.
001463394 77608 $$iPrint version:$$aIten, Raban.$$tArtificial intelligence for scientific discoveries.$$dCham : Springer, 2023$$z9783031270185$$w(OCoLC)1371854299
001463394 852__ $$bebk
001463394 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-27019-2$$zOnline Access$$91397441.1
001463394 909CO $$ooai:library.usi.edu:1463394$$pGLOBAL_SET
001463394 980__ $$aBIB
001463394 980__ $$aEBOOK
001463394 982__ $$aEbook
001463394 983__ $$aOnline
001463394 994__ $$a92$$bISE