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＜電子ブック＞
The Calabi–Yau Landscape : From Geometry, to Physics, to Machine Learning / by Yang-Hui He
(Lecture Notes in Mathematics. ISSN:16179692 ; 2293)

版	1st ed. 2021.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2021
本文言語	英語
大きさ	XVII, 206 p. 36 illus., 26 illus. in color : online resource
著者標目	*He, Yang-Hui author SpringerLink (Online service)
件　名	LCSH:Algebraic geometry LCSH:Mathematical physics LCSH:Machine learning LCSH:Computer software FREE:Algebraic Geometry FREE:Mathematical Physics FREE:Machine Learning FREE:Mathematical Software
一般注記	Can 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 HTTP:URL=https://doi.org/10.1007/978-3-030-77562-9

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