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001438661 020__ $$a9783030775629$$q(electronic bk.)
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001438661 0247_ $$a10.1007/978-3-030-77562-9$$2doi
001438661 035__ $$aSP(OCoLC)1262726106
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001438661 049__ $$aISEA
001438661 050_4 $$aQA3$$b.L28 no. 2293
001438661 050_4 $$aQC20.7.M24$$bH49 2021
001438661 08204 $$a516/.07$$223
001438661 1001_ $$aHe, Yang-Hui,$$d1975-
001438661 24514 $$aThe Calabi-Yau landscape :$$bfrom geometry, to physics, to machine learning /$$cYang-Hui He.
001438661 264_1 $$aCham, Switzerland :$$bSpringer,$$c2021.
001438661 300__ $$a1 online resource
001438661 336__ $$atext$$btxt$$2rdacontent
001438661 337__ $$acomputer$$bc$$2rdamedia
001438661 338__ $$aonline resource$$bcr$$2rdacarrier
001438661 4901_ $$aLecture Notes in Mathematics,$$x0075-8434 ;$$vvolume 2293
001438661 504__ $$aIncludes bibliographical references and index.
001438661 506__ $$aAccess limited to authorized users.
001438661 520__ $$aCan artificial intelligence learn mathematics? The question is at the heart of this original monograph bringing together theoretical physics, modern geometry, and data science. The study of Calabi-Yau manifolds lies at an exciting intersection between physics and mathematics. Recently, there has been much activity in applying machine learning to solve otherwise intractable problems, to conjecture new formulae, or to understand the underlying structure of mathematics. In this book, insights from string and quantum field theory are combined with powerful techniques from complex and algebraic geometry, then translated into algorithms with the ultimate aim of deriving new information about Calabi-Yau manifolds. While the motivation comes from mathematical physics, the techniques are purely mathematical and the theme is that of explicit calculations. The reader is guided through the theory and provided with explicit computer code in standard software such as SageMath, Python and Mathematica to gain hands-on experience in applications of artificial intelligence to geometry. Driven by data and written in an informal style, The Calabi-Yau Landscape makes cutting-edge topics in mathematical physics, geometry and machine learning readily accessible to graduate students and beyond. The overriding ambition is to introduce some modern mathematics to the physicist, some modern physics to the mathematician, and machine learning to both.
001438661 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed August 10, 2021).
001438661 650_0 $$aCalabi-Yau manifolds.
001438661 650_6 $$aVariétés de Calabi-Yau.
001438661 655_0 $$aElectronic books.
001438661 77608 $$iPrint version:$$aHe, Yang-Hui, 1975-$$tCalabi-Yau landscape.$$dCham, Switzerland : Springer, 2021$$z3030775615$$z9783030775612$$w(OCoLC)1249070465
001438661 830_0 $$aLecture notes in mathematics (Springer-Verlag) ;$$v2293.$$x0075-8434
001438661 852__ $$bebk
001438661 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-77562-9$$zOnline Access$$91397441.1
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