| 书目名称 | The Calabi–Yau Landscape | | 副标题 | From Geometry, to Ph | | 编辑 | Yang-Hui He | | 视频video | | | 概述 | The first monograph applying machine learning to problems of geometry.Provides a data-driven introduction to computational algebraic geometry.Delivers a quick introduction to modern data science, with | | 丛书名称 | Lecture Notes in Mathematics | | 图书封面 |  | | 描述 | 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 mathemat | | 出版日期 | Book 2021 | | 关键词 | Machine learning in algebraic geometry; Computational algebraic geometry; String theory; Supersymmetry; | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-77562-9 | | isbn_softcover | 978-3-030-77561-2 | | isbn_ebook | 978-3-030-77562-9Series ISSN 0075-8434 Series E-ISSN 1617-9692 | | issn_series | 0075-8434 | | copyright | Springer Nature Switzerland AG 2021 |
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发表于 2025-3-21 17:31:40
